{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 0, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"055b599a532327fd124e12a7\", \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"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}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"055b599a532327fd124e12a7\", \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"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}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 1, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 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\": 7, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.42424242424242425, \"mean_separation\": 0.27380952380952384, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.47916666666666674, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"034f3e803f1dd9eff9726b5e\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 14, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_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\": 0}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=0, Y=1\n\nExperiment 7:\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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=0, Y=1\n\nExperiment 7:\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\": [3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 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\": 7, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.42424242424242425, \"mean_separation\": 0.27380952380952384, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.47916666666666674, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"034f3e803f1dd9eff9726b5e\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 14, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_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\": 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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 2, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5142857142857142, \"mean_separation\": 0.27809523809523806, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5214285714285714, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"19945cbc44f4d180dab68d07886335844930b281314c69ca5d53eeea411cbe48\", \"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"383bc7130cec41b9785fa6f015dc95c8e8ade5e3232c5ed167acbaf67276a2d7\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"4b7065029bd41a5ea2c18191e7d076a2edc74738565a9f9b6c48ead7c12b741f\", \"6661b95527e8f896424d582014ae3af2c59f1fc215aea96886c3ba2e0b9a2859\", \"6cce82043df1b0451a2ad7a1fb402cf671426f311df5b5de54158db37f7351f1\", \"6e60177e6bed3f92bf67a3d5476276ba13525dba89dac8cac0c2ad971ff0efdd\", \"85877a8516d140c09203044fac0e054c45d59d4293d2d5255e6e06dd36ed2db5\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"ee9c4d40147d6beef2dc92f96edf439bf27c339e8062fd90bbc042ecaddc86d2\"]}, \"state_id\": \"06c4cc5d6f5e1a710ebc91d8\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"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\": 1, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: 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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, 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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, 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, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5142857142857142, \"mean_separation\": 0.27809523809523806, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5214285714285714, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"19945cbc44f4d180dab68d07886335844930b281314c69ca5d53eeea411cbe48\", \"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"383bc7130cec41b9785fa6f015dc95c8e8ade5e3232c5ed167acbaf67276a2d7\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"4b7065029bd41a5ea2c18191e7d076a2edc74738565a9f9b6c48ead7c12b741f\", \"6661b95527e8f896424d582014ae3af2c59f1fc215aea96886c3ba2e0b9a2859\", \"6cce82043df1b0451a2ad7a1fb402cf671426f311df5b5de54158db37f7351f1\", \"6e60177e6bed3f92bf67a3d5476276ba13525dba89dac8cac0c2ad971ff0efdd\", \"85877a8516d140c09203044fac0e054c45d59d4293d2d5255e6e06dd36ed2db5\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"ee9c4d40147d6beef2dc92f96edf439bf27c339e8062fd90bbc042ecaddc86d2\"]}, \"state_id\": \"06c4cc5d6f5e1a710ebc91d8\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"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\": 1, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 3, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"058fc7470f7352a91f02b453\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\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\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\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\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"058fc7470f7352a91f02b453\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 4, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"00a3be73cd866044bd2c4e2b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z2\": 0}}, {\"experiment_id\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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\": [2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"00a3be73cd866044bd2c4e2b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z2\": 0}}, {\"experiment_id\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 5, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5, \"mean_separation\": 0.27037037037037037, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5069444444444444, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"19945cbc44f4d180dab68d07886335844930b281314c69ca5d53eeea411cbe48\", \"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"383bc7130cec41b9785fa6f015dc95c8e8ade5e3232c5ed167acbaf67276a2d7\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"4b7065029bd41a5ea2c18191e7d076a2edc74738565a9f9b6c48ead7c12b741f\", \"6661b95527e8f896424d582014ae3af2c59f1fc215aea96886c3ba2e0b9a2859\", \"6cce82043df1b0451a2ad7a1fb402cf671426f311df5b5de54158db37f7351f1\", \"6e60177e6bed3f92bf67a3d5476276ba13525dba89dac8cac0c2ad971ff0efdd\", \"85877a8516d140c09203044fac0e054c45d59d4293d2d5255e6e06dd36ed2db5\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"ee9c4d40147d6beef2dc92f96edf439bf27c339e8062fd90bbc042ecaddc86d2\"]}, \"state_id\": \"048c6b26e5f942ea3c3c2529\", \"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\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=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, 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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, 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, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5, \"mean_separation\": 0.27037037037037037, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5069444444444444, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"19945cbc44f4d180dab68d07886335844930b281314c69ca5d53eeea411cbe48\", \"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"383bc7130cec41b9785fa6f015dc95c8e8ade5e3232c5ed167acbaf67276a2d7\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"4b7065029bd41a5ea2c18191e7d076a2edc74738565a9f9b6c48ead7c12b741f\", \"6661b95527e8f896424d582014ae3af2c59f1fc215aea96886c3ba2e0b9a2859\", \"6cce82043df1b0451a2ad7a1fb402cf671426f311df5b5de54158db37f7351f1\", \"6e60177e6bed3f92bf67a3d5476276ba13525dba89dac8cac0c2ad971ff0efdd\", \"85877a8516d140c09203044fac0e054c45d59d4293d2d5255e6e06dd36ed2db5\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"ee9c4d40147d6beef2dc92f96edf439bf27c339e8062fd90bbc042ecaddc86d2\"]}, \"state_id\": \"048c6b26e5f942ea3c3c2529\", \"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 6, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.42424242424242425, \"mean_separation\": 0.31313131313131315, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4696969696969697, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"03ef21e2d63046ded486fdce\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z2\": 0}}, {\"experiment_id\": 14, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 23, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 7:\n  inputs: X1=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 7:\n  inputs: X1=1, 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\": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.42424242424242425, \"mean_separation\": 0.31313131313131315, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4696969696969697, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"03ef21e2d63046ded486fdce\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z2\": 0}}, {\"experiment_id\": 14, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 23, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 7, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5714285714285714, \"mean_separation\": 0.32653061224489793, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5714285714285714, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"77e5ca3542706a2ca2942623054d43f07206067c7a012c58c31b5214e1991d15\", \"84adfd50416733f0573aee86ee00ad359768326e64facc3e73ee119d07a07550\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d9cde9cff26307bd18397d87a34f2bf64f5d50fea7a78dbd967408ac96539228\", \"f8d215a8066b188c14a8f5e98151cbcc0e76d5f6e0040207fe173d952ad8d557\"]}, \"state_id\": \"039eb58c3747d798c7b61928\", \"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\": 1, \"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\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\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=0, X3=1\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=0, Y=1\n\nExperiment 5:\n  inputs: X1=1, X2=1, 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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\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=0, Y=1\n\nExperiment 5:\n  inputs: X1=1, X2=1, 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, 6, 8, 9, 10, 11, 12, 13, 14, 15, 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\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5714285714285714, \"mean_separation\": 0.32653061224489793, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5714285714285714, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"77e5ca3542706a2ca2942623054d43f07206067c7a012c58c31b5214e1991d15\", \"84adfd50416733f0573aee86ee00ad359768326e64facc3e73ee119d07a07550\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d9cde9cff26307bd18397d87a34f2bf64f5d50fea7a78dbd967408ac96539228\", \"f8d215a8066b188c14a8f5e98151cbcc0e76d5f6e0040207fe173d952ad8d557\"]}, \"state_id\": \"039eb58c3747d798c7b61928\", \"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\": 1, \"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\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 8, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 6, 7, 8, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5294117647058824, \"mean_separation\": 0.28627450980392155, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5367647058823529, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"19945cbc44f4d180dab68d07886335844930b281314c69ca5d53eeea411cbe48\", \"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"383bc7130cec41b9785fa6f015dc95c8e8ade5e3232c5ed167acbaf67276a2d7\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"4b7065029bd41a5ea2c18191e7d076a2edc74738565a9f9b6c48ead7c12b741f\", \"6661b95527e8f896424d582014ae3af2c59f1fc215aea96886c3ba2e0b9a2859\", \"6cce82043df1b0451a2ad7a1fb402cf671426f311df5b5de54158db37f7351f1\", \"6e60177e6bed3f92bf67a3d5476276ba13525dba89dac8cac0c2ad971ff0efdd\", \"85877a8516d140c09203044fac0e054c45d59d4293d2d5255e6e06dd36ed2db5\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"ee9c4d40147d6beef2dc92f96edf439bf27c339e8062fd90bbc042ecaddc86d2\"]}, \"state_id\": \"018ded955fc44e264a084f6f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 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\": 1, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, 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\": [2, 3, 4, 6, 7, 8, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5294117647058824, \"mean_separation\": 0.28627450980392155, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5367647058823529, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"19945cbc44f4d180dab68d07886335844930b281314c69ca5d53eeea411cbe48\", \"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"383bc7130cec41b9785fa6f015dc95c8e8ade5e3232c5ed167acbaf67276a2d7\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"4b7065029bd41a5ea2c18191e7d076a2edc74738565a9f9b6c48ead7c12b741f\", \"6661b95527e8f896424d582014ae3af2c59f1fc215aea96886c3ba2e0b9a2859\", \"6cce82043df1b0451a2ad7a1fb402cf671426f311df5b5de54158db37f7351f1\", \"6e60177e6bed3f92bf67a3d5476276ba13525dba89dac8cac0c2ad971ff0efdd\", \"85877a8516d140c09203044fac0e054c45d59d4293d2d5255e6e06dd36ed2db5\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"ee9c4d40147d6beef2dc92f96edf439bf27c339e8062fd90bbc042ecaddc86d2\"]}, \"state_id\": \"018ded955fc44e264a084f6f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 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\": 1, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 9, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"4b281f0c85cad823411a5d6de1bc6db816afdc489e91292954ff40507b2939da\", \"c3298509ca1b2c95a8f3198e58d2d27fddd21c3e91675bbb6fb18adf6a3478cd\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"022e5f909037e9a1e274b1b9\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 14, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"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\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\n  intervention: none\n  observed: Z1=1, 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\nExperiment 4:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=1\n\nExperiment 5:\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=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=1, 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\nExperiment 4:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=1\n\nExperiment 5:\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\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"4b281f0c85cad823411a5d6de1bc6db816afdc489e91292954ff40507b2939da\", \"c3298509ca1b2c95a8f3198e58d2d27fddd21c3e91675bbb6fb18adf6a3478cd\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"022e5f909037e9a1e274b1b9\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 14, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"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\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 10, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5294117647058824, \"mean_separation\": 0.2867647058823529, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5018382352941176, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"02040db017dab75a2fd24c98\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 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\": 0, \"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}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\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\": [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, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5294117647058824, \"mean_separation\": 0.2867647058823529, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5018382352941176, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"02040db017dab75a2fd24c98\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 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\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 11, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 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\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4, \"mean_separation\": 0.23785714285714288, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4459821428571429, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1c438e00730c2114ec43cd8a3618709f10db4f266582e982242ba3d43e07e549\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"00e727d61b23e55da0949d84\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\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=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=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\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\": [1, 2, 3, 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\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4, \"mean_separation\": 0.23785714285714288, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4459821428571429, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1c438e00730c2114ec43cd8a3618709f10db4f266582e982242ba3d43e07e549\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"00e727d61b23e55da0949d84\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 12, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"016bdf7c564b4a3d97dbf7c9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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\": [3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"016bdf7c564b4a3d97dbf7c9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 13, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5882352941176471, \"mean_separation\": 0.31722689075630256, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5551470588235295, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"097180c5d46dde7320c2132ba2fe66b2f6691e47281d5c31a9bc10d240453de6\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b75b74456a66a467dece872bcf09fa07729d9245745b1b3d77e7afb36dae1ad5\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"004c45163e74c18a433814e8\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"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\": 0, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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\": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5882352941176471, \"mean_separation\": 0.31722689075630256, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5551470588235295, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"097180c5d46dde7320c2132ba2fe66b2f6691e47281d5c31a9bc10d240453de6\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b75b74456a66a467dece872bcf09fa07729d9245745b1b3d77e7afb36dae1ad5\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"004c45163e74c18a433814e8\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"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\": 0, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 14, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.30303030303030304, \"mean_separation\": 0.16161616161616163, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.30303030303030304, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"0133db34b65af0d6ff48d8b8\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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\": [3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.30303030303030304, \"mean_separation\": 0.16161616161616163, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.30303030303030304, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"0133db34b65af0d6ff48d8b8\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 15, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"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, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0168ad606454af37efba3e48\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"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\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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: set Z1=0\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=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=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: set Z1=0\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=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\": [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, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0168ad606454af37efba3e48\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"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\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 16, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4117647058823529, \"mean_separation\": 0.23529411764705882, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.411764705882353, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"025d4e0855c30f5781ff16d9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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\": [2, 3, 4, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4117647058823529, \"mean_separation\": 0.23529411764705882, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.411764705882353, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"025d4e0855c30f5781ff16d9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 7, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 17, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 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\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4, \"mean_separation\": 0.23785714285714288, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4459821428571429, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1c438e00730c2114ec43cd8a3618709f10db4f266582e982242ba3d43e07e549\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"02be286f93c1a9fe9007caba\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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\": [1, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 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\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4, \"mean_separation\": 0.23785714285714288, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4459821428571429, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1c438e00730c2114ec43cd8a3618709f10db4f266582e982242ba3d43e07e549\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"02be286f93c1a9fe9007caba\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 1, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 18, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 6, 7, 9, 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\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"4b281f0c85cad823411a5d6de1bc6db816afdc489e91292954ff40507b2939da\", \"c3298509ca1b2c95a8f3198e58d2d27fddd21c3e91675bbb6fb18adf6a3478cd\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"02ab2bfd2b0b739e72013e17\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"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\": 0, \"Z1\": 1, \"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\": 1, \"Z2\": 0}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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 Z2=0\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=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=0, Y=1\n\nExperiment 5:\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=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 Z2=0\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=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=0, Y=1\n\nExperiment 5:\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\": [0, 1, 2, 3, 4, 6, 7, 9, 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\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"4b281f0c85cad823411a5d6de1bc6db816afdc489e91292954ff40507b2939da\", \"c3298509ca1b2c95a8f3198e58d2d27fddd21c3e91675bbb6fb18adf6a3478cd\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"02ab2bfd2b0b739e72013e17\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"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\": 0, \"Z1\": 1, \"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\": 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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 19, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0055ce4916cf8318e3da7fe7\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, 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\": [2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0055ce4916cf8318e3da7fe7\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 20, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.45714285714285713, \"mean_separation\": 0.2235714285714286, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4191964285714286, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3c486722d9407e68035ba87d759fc64b111025943f92766c26056f072b997707\", \"5e971b03487926c733eb64e19edfab27d6fc61cb039b7f33a3e56ab92361f0db\", \"6fbe144ec8c0329a10b573e1f9fae4346333bd3b37dccf1a121298f158eb419f\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b621e7ee4e35557cfe78d22e025c530d5a9a5ef34f7f284a980fd96d5f02706c\", \"c4df8d03402f7120aeefed4b0d481409ea52d095a21470de2ce63c4ea2160fc1\", \"cc22710197819424f766df593d91c5b6dcbb76db63676b359457ba4cfda28900\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"e447453e1305f35b3e2536654ec22d22af23c2d4f34160000a1b3dccfb9c54b2\", \"e9b854ddd7456c5c06746a2b3aa21749fb00f7aa1c2de23214c75fcd712a462f\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\", \"f2091f506c47c8b6157cb873c9de6ccc9f60d039fe8743930da6798424456d86\"]}, \"state_id\": \"065e3dc9db30d5ddc7c08af9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, 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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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=1, 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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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, 14, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.45714285714285713, \"mean_separation\": 0.2235714285714286, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4191964285714286, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3c486722d9407e68035ba87d759fc64b111025943f92766c26056f072b997707\", \"5e971b03487926c733eb64e19edfab27d6fc61cb039b7f33a3e56ab92361f0db\", \"6fbe144ec8c0329a10b573e1f9fae4346333bd3b37dccf1a121298f158eb419f\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b621e7ee4e35557cfe78d22e025c530d5a9a5ef34f7f284a980fd96d5f02706c\", \"c4df8d03402f7120aeefed4b0d481409ea52d095a21470de2ce63c4ea2160fc1\", \"cc22710197819424f766df593d91c5b6dcbb76db63676b359457ba4cfda28900\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"e447453e1305f35b3e2536654ec22d22af23c2d4f34160000a1b3dccfb9c54b2\", \"e9b854ddd7456c5c06746a2b3aa21749fb00f7aa1c2de23214c75fcd712a462f\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\", \"f2091f506c47c8b6157cb873c9de6ccc9f60d039fe8743930da6798424456d86\"]}, \"state_id\": \"065e3dc9db30d5ddc7c08af9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 21, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.42424242424242425, \"mean_separation\": 0.31313131313131315, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4696969696969697, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"050c73682c9ac56f463b20d4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z2\": 0}}, {\"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\": 0}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"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\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=1\n\nExperiment 7:\n  inputs: X1=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=1\n\nExperiment 7:\n  inputs: X1=1, 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\": [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.42424242424242425, \"mean_separation\": 0.31313131313131315, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4696969696969697, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"050c73682c9ac56f463b20d4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z2\": 0}}, {\"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\": 0}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"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\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 22, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"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, 24, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.3075396825396825, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5381944444444444, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"4b281f0c85cad823411a5d6de1bc6db816afdc489e91292954ff40507b2939da\", \"a18a83644b4ed2181215ffa77cc4d81724650a34591e9dc29e5994374bdadf3d\", \"a4b57267012144b9b0bbcb0c94893e9491a02652556ab514e037151f02f081b1\", \"c2847dff8686e8956aac934a12075fd29dcec366a3011c00ae54fcdc2b5843c8\", \"c3298509ca1b2c95a8f3198e58d2d27fddd21c3e91675bbb6fb18adf6a3478cd\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f37a0b32f5f365d480abc4eee2df018aab996d5f2f4991c0a9df49dbbaf46538\"]}, \"state_id\": \"02a9aaac8f5cdb1bec3d7026\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"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\": 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\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\n\nExperiment 2:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\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=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=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: set Z2=0\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=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\": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 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\": 0.5555555555555556, \"mean_separation\": 0.3075396825396825, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5381944444444444, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"4b281f0c85cad823411a5d6de1bc6db816afdc489e91292954ff40507b2939da\", \"a18a83644b4ed2181215ffa77cc4d81724650a34591e9dc29e5994374bdadf3d\", \"a4b57267012144b9b0bbcb0c94893e9491a02652556ab514e037151f02f081b1\", \"c2847dff8686e8956aac934a12075fd29dcec366a3011c00ae54fcdc2b5843c8\", \"c3298509ca1b2c95a8f3198e58d2d27fddd21c3e91675bbb6fb18adf6a3478cd\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f37a0b32f5f365d480abc4eee2df018aab996d5f2f4991c0a9df49dbbaf46538\"]}, \"state_id\": \"02a9aaac8f5cdb1bec3d7026\", \"visible_experiments\": [{\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"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\": 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\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 23, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.15238095238095237, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"03e89dd1b1edd9816f933dac\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"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}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0, X2=1, X3=1\n  intervention: set Z1=1\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\nExperiment 5:\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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\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\nExperiment 5:\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, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.15238095238095237, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"03e89dd1b1edd9816f933dac\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"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}}, {\"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 24, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"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, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"058631be06bacc7458c99171\", \"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\": 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\": 24, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: 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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\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=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"058631be06bacc7458c99171\", \"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\": 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\": 24, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 25, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 8, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"00cb07ad67b54f8845e3a6b4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 36, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=1\n\nExperiment 7:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 8:\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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=1\n\nExperiment 7:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 8:\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\": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 8, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"00cb07ad67b54f8845e3a6b4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 26, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 6, 7, 8, 9, 10, 11, 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\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4, \"mean_separation\": 0.23785714285714288, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4459821428571429, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1c438e00730c2114ec43cd8a3618709f10db4f266582e982242ba3d43e07e549\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"0462ec664f79f406c07a528e\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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\": [2, 3, 4, 6, 7, 8, 9, 10, 11, 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\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4, \"mean_separation\": 0.23785714285714288, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4459821428571429, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1c438e00730c2114ec43cd8a3618709f10db4f266582e982242ba3d43e07e549\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"0462ec664f79f406c07a528e\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 27, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"012a8c9dc48078431f444551\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 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\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 7:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 7:\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\": [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, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"012a8c9dc48078431f444551\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 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\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 28, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 4, 5, 6, 7, 8, 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\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.16326530612244897, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0042f1ed4666f9803c63744d\", \"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\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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, 4, 5, 6, 7, 8, 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\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.16326530612244897, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0042f1ed4666f9803c63744d\", \"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\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 29, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 4, 6, 7, 8, 10, 11, 12, 13, 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\": 7, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6060606060606061, \"mean_separation\": 0.31616161616161614, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5928030303030303, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"19945cbc44f4d180dab68d07886335844930b281314c69ca5d53eeea411cbe48\", \"1b0afdfc8e17f0ff8147df740ad91f3c8bd45ff39f989057ff6a953bfb59610c\", \"2ac42cb9afcc2ee4f696938b56127b39655a2487421a6b481584e48108f9e29a\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"4b7065029bd41a5ea2c18191e7d076a2edc74738565a9f9b6c48ead7c12b741f\", \"5d0686b6e82b533708cf767106f66a625540a36b2b2fc9defac055df534b2993\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"87799aa0c3a1c5f2ddff705c372ac2e82213b4663a5c575bfdbbde0e0de668e1\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"ce7414fc423b7c1fe4626c289c80ea94ba7e53774b671375fa3036786d47e96d\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"ee65835b745d55ccec05013f0dff8ecd8d627bd23305c3498d17dc5aa98dd6af\"]}, \"state_id\": \"06e71c8ce4318a075edda32c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 14, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=0, 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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=0, 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\": [3, 4, 6, 7, 8, 10, 11, 12, 13, 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\": 7, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6060606060606061, \"mean_separation\": 0.31616161616161614, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5928030303030303, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"19945cbc44f4d180dab68d07886335844930b281314c69ca5d53eeea411cbe48\", \"1b0afdfc8e17f0ff8147df740ad91f3c8bd45ff39f989057ff6a953bfb59610c\", \"2ac42cb9afcc2ee4f696938b56127b39655a2487421a6b481584e48108f9e29a\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"4b7065029bd41a5ea2c18191e7d076a2edc74738565a9f9b6c48ead7c12b741f\", \"5d0686b6e82b533708cf767106f66a625540a36b2b2fc9defac055df534b2993\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"87799aa0c3a1c5f2ddff705c372ac2e82213b4663a5c575bfdbbde0e0de668e1\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"ce7414fc423b7c1fe4626c289c80ea94ba7e53774b671375fa3036786d47e96d\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"ee65835b745d55ccec05013f0dff8ecd8d627bd23305c3498d17dc5aa98dd6af\"]}, \"state_id\": \"06e71c8ce4318a075edda32c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 14, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 30, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"03f29e9df2af5a25f6cdfffc\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"03f29e9df2af5a25f6cdfffc\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 31, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.16326530612244897, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"007005d0875611fe124ea238\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"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\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, 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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, 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, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.16326530612244897, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"007005d0875611fe124ea238\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"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\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 32, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.21333333333333335, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"75dc7f7a00c7c6e3d54a0bdd03f5752e40ba48fdbd733c949510e80581d9dd6b\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f81316d3e2e3727a5b24e6bb253e0524f80f0e53b00bb878542220bf99f8e0b6\"]}, \"state_id\": \"040b6bd062e9b653c0349e16\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, 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=0\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\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, 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=0, 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=0\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\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, 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, 5, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.21333333333333335, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"75dc7f7a00c7c6e3d54a0bdd03f5752e40ba48fdbd733c949510e80581d9dd6b\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f81316d3e2e3727a5b24e6bb253e0524f80f0e53b00bb878542220bf99f8e0b6\"]}, \"state_id\": \"040b6bd062e9b653c0349e16\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 33, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.19607843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"00cb5d1f1beb9d66f58e1361\", \"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\": 0, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, 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\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, 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\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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, 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, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.19607843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"00cb5d1f1beb9d66f58e1361\", \"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\": 0, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 34, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 9, 10, 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\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.16326530612244897, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"033acfe75e512773c9e77c8c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, 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=0\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\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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=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=0\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\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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, 9, 10, 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\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.16326530612244897, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"033acfe75e512773c9e77c8c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 35, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3888888888888889, \"mean_separation\": 0.22962962962962966, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.43055555555555564, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1936e5f64d2147d806dcad159ab53d649419292b426473370728347751cbbf58\", \"4379ceed8feb46539a3cf42b545a3b7aeec55a27371eb756cf1987e7f115c269\", \"4d19dc11827c936b35b283eda0df655e5ff7da09a9c00a5afa874760bf278afa\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"74d0315ba53a265c1ffa88305031b254063225b75b33d03a821dfda40d532f16\", \"855f657bfd48224ceb81bf6fb3629f930055e57df0798c8e80e2138b00f9dd68\", \"949558e07e2475965f8f962233921652ffa4e6f600d69b435fdfb38045701968\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"a6d51141a6bfc19cfb68d1aa563d10942d9a7352b20ae9db3dbd83ab800b4a33\", \"b398b9489aa36a6d9491d8285f9d0003c9b2ab3a41acc239f65f17e0508136c1\", \"c1eaa108c6a0282aeb74dddc29cc96db8fd0c036c7f352cc5b511a5ea60cc677\", \"ce170ee141cc0973e78d54990e2dbbc8c6e52e4d03e3057e14248fad368c8288\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dc487188998454049b5e0924fa29cdacc0b7a06a33bc9f92ae864c6583e73100\", \"f107c18a33948f2640233a48eb12c60c4d2f9035b7d72213a5a0a3227c163566\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"064220b01c74b7ac08adafcc\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"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}}, {\"experiment_id\": 24, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\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\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=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: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\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\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\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 22, 23, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3888888888888889, \"mean_separation\": 0.22962962962962966, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.43055555555555564, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1936e5f64d2147d806dcad159ab53d649419292b426473370728347751cbbf58\", \"4379ceed8feb46539a3cf42b545a3b7aeec55a27371eb756cf1987e7f115c269\", \"4d19dc11827c936b35b283eda0df655e5ff7da09a9c00a5afa874760bf278afa\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"74d0315ba53a265c1ffa88305031b254063225b75b33d03a821dfda40d532f16\", \"855f657bfd48224ceb81bf6fb3629f930055e57df0798c8e80e2138b00f9dd68\", \"949558e07e2475965f8f962233921652ffa4e6f600d69b435fdfb38045701968\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"a6d51141a6bfc19cfb68d1aa563d10942d9a7352b20ae9db3dbd83ab800b4a33\", \"b398b9489aa36a6d9491d8285f9d0003c9b2ab3a41acc239f65f17e0508136c1\", \"c1eaa108c6a0282aeb74dddc29cc96db8fd0c036c7f352cc5b511a5ea60cc677\", \"ce170ee141cc0973e78d54990e2dbbc8c6e52e4d03e3057e14248fad368c8288\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dc487188998454049b5e0924fa29cdacc0b7a06a33bc9f92ae864c6583e73100\", \"f107c18a33948f2640233a48eb12c60c4d2f9035b7d72213a5a0a3227c163566\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"064220b01c74b7ac08adafcc\", \"visible_experiments\": [{\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"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}}, {\"experiment_id\": 24, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 36, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"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, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.1851851851851852, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.27777777777777785, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"592b801b25456a2be278465f8952c666c51d31c2cb03fc65633633739e4d0c25\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3298509ca1b2c95a8f3198e58d2d27fddd21c3e91675bbb6fb18adf6a3478cd\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"01562fe2f860842a3db4179b\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"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}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=1, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=1, 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=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=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=1, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=1, 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=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, 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, 33, 34, 35, 36, 37, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.1851851851851852, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.27777777777777785, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"592b801b25456a2be278465f8952c666c51d31c2cb03fc65633633739e4d0c25\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3298509ca1b2c95a8f3198e58d2d27fddd21c3e91675bbb6fb18adf6a3478cd\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"01562fe2f860842a3db4179b\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 37, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5294117647058824, \"mean_separation\": 0.29411764705882354, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5147058823529412, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"01c9527428ea6d6c98520810\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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\": [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, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5294117647058824, \"mean_separation\": 0.29411764705882354, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5147058823529412, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"01c9527428ea6d6c98520810\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 38, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3888888888888889, \"mean_separation\": 0.2074074074074074, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.3888888888888889, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41e64fcebdaa053324913c08696ffa851612fe7b6170454a02faa6c7b770a9c3\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"5a52882745917b71de30b9c55c4cb589cae55f534a099348c459e3bcc5c298b4\", \"71bc95ccfe0895f40c73e4349f305fb72146892cb4bcd4ae967da0689a859244\", \"8296027227313adb4f914ab47a197ed600355e93e9f3755d4712e6d5d6c61a70\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"a18a83644b4ed2181215ffa77cc4d81724650a34591e9dc29e5994374bdadf3d\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"c2847dff8686e8956aac934a12075fd29dcec366a3011c00ae54fcdc2b5843c8\", \"d37e126e558e3ee18fadde1e45f6e1b427df97d9e5f6cbe78122693b84e67bec\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dd95e4e8161fdac3e6de81a55be9f759d978f3844183745d2d9171e5eed2ee1a\", \"ea51c29cf26cfbfa59b0166538c10a2700421a534c819af2dfe3d92840772ebc\", \"ed00187274dbce98eb44962c2fff6d0f383f299f2569029a4cb0f87a1c5b62e4\"]}, \"state_id\": \"03ea3dcd234e1e77ec64c1b2\", \"visible_experiments\": [{\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"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}}, {\"experiment_id\": 26, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"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}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, X3=1\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=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=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=1\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=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=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, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3888888888888889, \"mean_separation\": 0.2074074074074074, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.3888888888888889, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41e64fcebdaa053324913c08696ffa851612fe7b6170454a02faa6c7b770a9c3\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"5a52882745917b71de30b9c55c4cb589cae55f534a099348c459e3bcc5c298b4\", \"71bc95ccfe0895f40c73e4349f305fb72146892cb4bcd4ae967da0689a859244\", \"8296027227313adb4f914ab47a197ed600355e93e9f3755d4712e6d5d6c61a70\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"a18a83644b4ed2181215ffa77cc4d81724650a34591e9dc29e5994374bdadf3d\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"c2847dff8686e8956aac934a12075fd29dcec366a3011c00ae54fcdc2b5843c8\", \"d37e126e558e3ee18fadde1e45f6e1b427df97d9e5f6cbe78122693b84e67bec\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dd95e4e8161fdac3e6de81a55be9f759d978f3844183745d2d9171e5eed2ee1a\", \"ea51c29cf26cfbfa59b0166538c10a2700421a534c819af2dfe3d92840772ebc\", \"ed00187274dbce98eb44962c2fff6d0f383f299f2569029a4cb0f87a1c5b62e4\"]}, \"state_id\": \"03ea3dcd234e1e77ec64c1b2\", \"visible_experiments\": [{\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"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}}, {\"experiment_id\": 26, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 39, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.19607843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"057745edc5fa9295ffd7bbb8\", \"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\": 0, \"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\": 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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.19607843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"057745edc5fa9295ffd7bbb8\", \"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\": 0, \"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\": 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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 40, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 4, 6, 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, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.36363636363636365, \"mean_separation\": 0.2683982683982684, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4696969696969697, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"efe12d59a775be045b823b4e74f28fec605d56b654deb15f0b36c7a9472758fc\"]}, \"state_id\": \"00e18fb41ddac024e75360b9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\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\": [2, 4, 6, 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, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.36363636363636365, \"mean_separation\": 0.2683982683982684, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4696969696969697, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"efe12d59a775be045b823b4e74f28fec605d56b654deb15f0b36c7a9472758fc\"]}, \"state_id\": \"00e18fb41ddac024e75360b9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z2\": 0}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 41, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5714285714285714, \"mean_separation\": 0.3230952380952381, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.6058035714285714, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0e8433a007da914d4dd3706b2fa112ba29e3e432b3f7bdd88aa5534617dcb322\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3c486722d9407e68035ba87d759fc64b111025943f92766c26056f072b997707\", \"63b4358687ce4e476ff6c76179c7b3118115d9efc69516f11fdb4a38c31fb9e1\", \"668f2cd41ecf29265d5a4060ec2a0aa57fd1857ad6fc3f6c51749fee7e500d73\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"87706637b6b68c19e5de02ad43ca1fd551d6f28cd45a5566cb183012015a3063\", \"88057ca012d15a1b3cb20aa0650557381a8bee6a702fcf6ba4bf2634592c80cc\", \"989bcc7abd5bd7c4462caa1cf1f59ac7d6515d4a464258cc3296d869374de441\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"cc22710197819424f766df593d91c5b6dcbb76db63676b359457ba4cfda28900\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"e7ab997b51f342440d32843d07bdcc8334d13ade74c3150f8f39e510eb3609fb\"]}, \"state_id\": \"03edad147654eb5c3530d99a\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=1, X3=0\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=0, Z2=0, 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\nExperiment 5:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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=0, X3=0\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 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=0, Z2=0, 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\nExperiment 5:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5714285714285714, \"mean_separation\": 0.3230952380952381, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.6058035714285714, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0e8433a007da914d4dd3706b2fa112ba29e3e432b3f7bdd88aa5534617dcb322\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3c486722d9407e68035ba87d759fc64b111025943f92766c26056f072b997707\", \"63b4358687ce4e476ff6c76179c7b3118115d9efc69516f11fdb4a38c31fb9e1\", \"668f2cd41ecf29265d5a4060ec2a0aa57fd1857ad6fc3f6c51749fee7e500d73\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"87706637b6b68c19e5de02ad43ca1fd551d6f28cd45a5566cb183012015a3063\", \"88057ca012d15a1b3cb20aa0650557381a8bee6a702fcf6ba4bf2634592c80cc\", \"989bcc7abd5bd7c4462caa1cf1f59ac7d6515d4a464258cc3296d869374de441\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"cc22710197819424f766df593d91c5b6dcbb76db63676b359457ba4cfda28900\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"e7ab997b51f342440d32843d07bdcc8334d13ade74c3150f8f39e510eb3609fb\"]}, \"state_id\": \"03edad147654eb5c3530d99a\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 42, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"03e785bb64aaba20cd64a7d9\", \"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\": 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\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=1\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=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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=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=0\n\nExperiment 3:\n  inputs: X1=0, 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=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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, 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, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"03e785bb64aaba20cd64a7d9\", \"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\": 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\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 43, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 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, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5294117647058824, \"mean_separation\": 0.29411764705882354, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5147058823529412, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"030f6f59bde618791ea5d9ce\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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\": [2, 3, 4, 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, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5294117647058824, \"mean_separation\": 0.29411764705882354, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5147058823529412, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"030f6f59bde618791ea5d9ce\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 8, \"inputs\": {\"X1\": 0, \"X2\": 0, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 44, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.15238095238095237, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"057e761fbfafc3fc695ffb07\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=1, 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=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=1, 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=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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, 10, 11, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.15238095238095237, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"057e761fbfafc3fc695ffb07\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 45, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"0065c5b1c271e48f38c98f08\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"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}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\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=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\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\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=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\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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\": [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, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"0065c5b1c271e48f38c98f08\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"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}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 46, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5142857142857142, \"mean_separation\": 0.2857142857142857, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"00f4597b4f5b12a848fe6055\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, 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=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=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=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5142857142857142, \"mean_separation\": 0.2857142857142857, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"00f4597b4f5b12a848fe6055\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 47, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.1568627450980392, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"5b76549a2887f710af826f4e246770b098e9715399ef99f0d10b0564483beca8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"ba4005e87ac1f33b5fe86e3d5a1199b9556d721a292e697dd553053772cad73f\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"001d24c44ad972489e9ccb89\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 31, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, 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\": [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, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.1568627450980392, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"5b76549a2887f710af826f4e246770b098e9715399ef99f0d10b0564483beca8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"ba4005e87ac1f33b5fe86e3d5a1199b9556d721a292e697dd553053772cad73f\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"001d24c44ad972489e9ccb89\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 31, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 48, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"020de0c16c1a2af846ff914f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\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\": [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, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"020de0c16c1a2af846ff914f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0, \"Z1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 49, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 8, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"00321ae85cefc72a5b2a170b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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\": 26, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=1\n\nExperiment 8:\n  inputs: X1=1, X2=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=1\n\nExperiment 8:\n  inputs: X1=1, X2=1, 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\": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 8, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"00321ae85cefc72a5b2a170b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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\": 26, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 50, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 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, 30, 31, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.1568627450980392, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"00ebf03f071850060fa93532\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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\": [3, 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, 30, 31, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.1568627450980392, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"00ebf03f071850060fa93532\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 51, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 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, 34, 35, 36, 37, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"002593f969700142a5fb96b4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, X3=0\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 Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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 Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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\": [2, 3, 4, 5, 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, 34, 35, 36, 37, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"002593f969700142a5fb96b4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 52, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.16326530612244897, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0212103c32301d2ce0974c06\", \"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\": 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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\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=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\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=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.16326530612244897, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0212103c32301d2ce0974c06\", \"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\": 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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 53, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 30, 31, 32, 33, 34, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.29444444444444445, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5520833333333334, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"03f1d0f9dc54ea06f12737e5e16c257ac7d50346d3dc2d38dd62a460f6fdf3e3\", \"1d5a5451751a7e4dd8665a585d6bf1602bbd859cbdc61190c9adf41b0378ee34\", \"2b02a7e47dae7ef515b4577d8de98f21fc562feda5db17353230e6d5466714fa\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"68ed0b460257ea77241316b54ff21eb643ff0bce3480c49bb201c14d5f6042fd\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"acf9e7599f917f06541611a378977439abd33169d3b6c16e930054241e05c1b0\", \"b687c7c6ea3fe0d606f9e535b2188d0106794d4cc9118eca4a3519457b5ba0cd\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"da8ed270e5c23476b1b0fbd98b93a2e872fae2d9aaa8e69d2aa00ccd13d5a119\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"fc18bb99f3723a86c375e828c01d1d0bffddcdd2b37b04cbd21dc1b058716d7f\"]}, \"state_id\": \"01fadd38292ed6250e8c9c52\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 15, \"inputs\": {\"X1\": 0, \"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}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, 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=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: set Z2=0\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=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\": [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, 30, 31, 32, 33, 34, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5555555555555556, \"mean_separation\": 0.29444444444444445, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5520833333333334, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"03f1d0f9dc54ea06f12737e5e16c257ac7d50346d3dc2d38dd62a460f6fdf3e3\", \"1d5a5451751a7e4dd8665a585d6bf1602bbd859cbdc61190c9adf41b0378ee34\", \"2b02a7e47dae7ef515b4577d8de98f21fc562feda5db17353230e6d5466714fa\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"68ed0b460257ea77241316b54ff21eb643ff0bce3480c49bb201c14d5f6042fd\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"acf9e7599f917f06541611a378977439abd33169d3b6c16e930054241e05c1b0\", \"b687c7c6ea3fe0d606f9e535b2188d0106794d4cc9118eca4a3519457b5ba0cd\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"da8ed270e5c23476b1b0fbd98b93a2e872fae2d9aaa8e69d2aa00ccd13d5a119\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"fc18bb99f3723a86c375e828c01d1d0bffddcdd2b37b04cbd21dc1b058716d7f\"]}, \"state_id\": \"01fadd38292ed6250e8c9c52\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 15, \"inputs\": {\"X1\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 54, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"02bd5c243994c43f314f454c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 31, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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\": [2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"02bd5c243994c43f314f454c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 31, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 55, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5294117647058824, \"mean_separation\": 0.29411764705882354, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5147058823529412, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"008bfce337780497fac13f19\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"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}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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: none\n  observed: Z1=1, 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\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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: none\n  observed: Z1=1, 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\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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\": [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, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5294117647058824, \"mean_separation\": 0.29411764705882354, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5147058823529412, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"008bfce337780497fac13f19\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"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}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 56, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5882352941176471, \"mean_separation\": 0.332843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.6240808823529412, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"097180c5d46dde7320c2132ba2fe66b2f6691e47281d5c31a9bc10d240453de6\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"7a3588d94c2aacf2b7d206a8e49dcfc5d163132d1bee706b33b98ac7e0da2368\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b75b74456a66a467dece872bcf09fa07729d9245745b1b3d77e7afb36dae1ad5\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"e7afb6b3d755be10464664ffbdea0f680d0e03031ca90ff388aaf4bac31ea9de\", \"ee8d593aefdc3ef1ccb8912dfa87fef4bccfcc1c33dc4be313e297554203ec28\", \"f10df40a5e367a10c637eb1edcd2378322b6c5b1ae7fdd37676136dbed58547c\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"02b92045deec49ce8edefb19\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=1\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=1\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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\": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5882352941176471, \"mean_separation\": 0.332843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.6240808823529412, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"097180c5d46dde7320c2132ba2fe66b2f6691e47281d5c31a9bc10d240453de6\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"7a3588d94c2aacf2b7d206a8e49dcfc5d163132d1bee706b33b98ac7e0da2368\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b75b74456a66a467dece872bcf09fa07729d9245745b1b3d77e7afb36dae1ad5\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"e7afb6b3d755be10464664ffbdea0f680d0e03031ca90ff388aaf4bac31ea9de\", \"ee8d593aefdc3ef1ccb8912dfa87fef4bccfcc1c33dc4be313e297554203ec28\", \"f10df40a5e367a10c637eb1edcd2378322b6c5b1ae7fdd37676136dbed58547c\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"02b92045deec49ce8edefb19\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 57, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"058088f3bf308d3175de69d2\", \"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\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: set Z1=0\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=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=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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=0, X3=0\n  intervention: set Z1=0\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=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=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"058088f3bf308d3175de69d2\", \"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\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 58, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.15873015873015875, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.27777777777777785, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b621e7ee4e35557cfe78d22e025c530d5a9a5ef34f7f284a980fd96d5f02706c\", \"c4df8d03402f7120aeefed4b0d481409ea52d095a21470de2ce63c4ea2160fc1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\", \"f2091f506c47c8b6157cb873c9de6ccc9f60d039fe8743930da6798424456d86\"]}, \"state_id\": \"00011532d58a14ffda2f7f2f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"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}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, Z2=1, 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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, 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=1, X3=0\n  intervention: set Z2=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, 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, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 4, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2777777777777778, \"mean_separation\": 0.15873015873015875, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.27777777777777785, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b621e7ee4e35557cfe78d22e025c530d5a9a5ef34f7f284a980fd96d5f02706c\", \"c4df8d03402f7120aeefed4b0d481409ea52d095a21470de2ce63c4ea2160fc1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\", \"f2091f506c47c8b6157cb873c9de6ccc9f60d039fe8743930da6798424456d86\"]}, \"state_id\": \"00011532d58a14ffda2f7f2f\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"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}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 59, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 7, 8, 9, 10, 11, 12, 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\": 0.5555555555555556, \"mean_separation\": 0.2787037037037037, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5225694444444444, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"3dcddc4d6937231006d7ecc618b1f6041f368edf4f7bca312d0951ae0e91c24a\", \"66e15a0c1fc4c186a40a2b46609efc42752940bdc82123cb089a8706ce491969\", \"70b0e8376ef5aeeea917b6d236b5fc5284984fbdab7d5e1dc0e6cd6279a2edb0\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"88700496ed2c5b81c1f4d28a6bd343f0d35fa6bc40cbfaa369bd2af7f615f1eb\", \"a18a83644b4ed2181215ffa77cc4d81724650a34591e9dc29e5994374bdadf3d\", \"c2847dff8686e8956aac934a12075fd29dcec366a3011c00ae54fcdc2b5843c8\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e1d9c6431294785ffb8d679ac5103541abc1f8c81822355a1b3e9243e6707116\", \"e6663b1d233e9e3ae3423c7c6b37aaac5e4d0a4a129fe04aa7c1fab1236c1d8a\", \"e75af8eac75842c878ff28a4c82357fd81c1ba38fb28f2747a382d8f92f7c3cf\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"ea9d1c130884de30b0b321f4a4fa5b16ffd1dab5d8df2c40132770754cbae38c\"]}, \"state_id\": \"026286383a514c3de5bdcac9\", \"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\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\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=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=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\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=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, 7, 8, 9, 10, 11, 12, 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\": 0.5555555555555556, \"mean_separation\": 0.2787037037037037, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5225694444444444, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"3dcddc4d6937231006d7ecc618b1f6041f368edf4f7bca312d0951ae0e91c24a\", \"66e15a0c1fc4c186a40a2b46609efc42752940bdc82123cb089a8706ce491969\", \"70b0e8376ef5aeeea917b6d236b5fc5284984fbdab7d5e1dc0e6cd6279a2edb0\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"88700496ed2c5b81c1f4d28a6bd343f0d35fa6bc40cbfaa369bd2af7f615f1eb\", \"a18a83644b4ed2181215ffa77cc4d81724650a34591e9dc29e5994374bdadf3d\", \"c2847dff8686e8956aac934a12075fd29dcec366a3011c00ae54fcdc2b5843c8\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e1d9c6431294785ffb8d679ac5103541abc1f8c81822355a1b3e9243e6707116\", \"e6663b1d233e9e3ae3423c7c6b37aaac5e4d0a4a129fe04aa7c1fab1236c1d8a\", \"e75af8eac75842c878ff28a4c82357fd81c1ba38fb28f2747a382d8f92f7c3cf\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"ea9d1c130884de30b0b321f4a4fa5b16ffd1dab5d8df2c40132770754cbae38c\"]}, \"state_id\": \"026286383a514c3de5bdcac9\", \"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\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 60, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"02fdcfc3a055ccc54372358d\", \"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\": 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\": 19, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"02fdcfc3a055ccc54372358d\", \"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\": 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\": 19, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 61, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 6, 7, 8, 9, 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\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4117647058823529, \"mean_separation\": 0.2657563025210084, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4650735294117647, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"0369435ab59dedd7bf4f569c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 6:\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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 6:\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\": [2, 3, 4, 6, 7, 8, 9, 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\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4117647058823529, \"mean_separation\": 0.2657563025210084, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4650735294117647, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"0369435ab59dedd7bf4f569c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 62, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.42424242424242425, \"mean_separation\": 0.22626262626262628, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.42424242424242425, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"75dc7f7a00c7c6e3d54a0bdd03f5752e40ba48fdbd733c949510e80581d9dd6b\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f81316d3e2e3727a5b24e6bb253e0524f80f0e53b00bb878542220bf99f8e0b6\"]}, \"state_id\": \"05700b7d1b6db694d30dda20\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, 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\": [3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.42424242424242425, \"mean_separation\": 0.22626262626262628, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.42424242424242425, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"75dc7f7a00c7c6e3d54a0bdd03f5752e40ba48fdbd733c949510e80581d9dd6b\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f81316d3e2e3727a5b24e6bb253e0524f80f0e53b00bb878542220bf99f8e0b6\"]}, \"state_id\": \"05700b7d1b6db694d30dda20\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 63, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"0119bc2d849d2b000d2c7b57\", \"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\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 23, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=1, 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=1\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=0\n\nExperiment 5:\n  inputs: X1=1, 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: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=1, 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=1\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=0\n\nExperiment 5:\n  inputs: X1=1, 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\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 19, 20, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"0119bc2d849d2b000d2c7b57\", \"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\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 23, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 64, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"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, 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\": 0.33035714285714285, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.578125, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"47ab0964af29109b1f2a5a2c80cc567d1af2a2278a4bcedd62fd162ac660a79e\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"9775433d5f9ca59b940c774bd532f826b6f31fbd5170052364e658164d79256a\", \"c2847dff8686e8956aac934a12075fd29dcec366a3011c00ae54fcdc2b5843c8\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"ed00187274dbce98eb44962c2fff6d0f383f299f2569029a4cb0f87a1c5b62e4\"]}, \"state_id\": \"028e564cc41765652f471e4f\", \"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\": 0, \"Z2\": 0}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: 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=0, Y=0\n\nExperiment 3:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=1\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=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=1, X2=0, X3=0\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 Z1=1\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=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, 21, 22, 23, 24, 25, 26, 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\": 0.33035714285714285, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.578125, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"47ab0964af29109b1f2a5a2c80cc567d1af2a2278a4bcedd62fd162ac660a79e\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"9775433d5f9ca59b940c774bd532f826b6f31fbd5170052364e658164d79256a\", \"c2847dff8686e8956aac934a12075fd29dcec366a3011c00ae54fcdc2b5843c8\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"ed00187274dbce98eb44962c2fff6d0f383f299f2569029a4cb0f87a1c5b62e4\"]}, \"state_id\": \"028e564cc41765652f471e4f\", \"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\": 0, \"Z2\": 0}}, {\"experiment_id\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 65, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 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\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.47058823529411764, \"mean_separation\": 0.2301470588235294, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.43152573529411764, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"2b02a7e47dae7ef515b4577d8de98f21fc562feda5db17353230e6d5466714fa\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"68ed0b460257ea77241316b54ff21eb643ff0bce3480c49bb201c14d5f6042fd\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a3f07ca5c82e7df5fd771be528d067d1e547adbaa46b6c75ca5762e7b0c71e7f\", \"acf9e7599f917f06541611a378977439abd33169d3b6c16e930054241e05c1b0\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"fc18bb99f3723a86c375e828c01d1d0bffddcdd2b37b04cbd21dc1b058716d7f\"]}, \"state_id\": \"04e3977b0ae520d1899120f3\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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\": [2, 3, 4, 5, 6, 7, 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\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.47058823529411764, \"mean_separation\": 0.2301470588235294, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.43152573529411764, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"2b02a7e47dae7ef515b4577d8de98f21fc562feda5db17353230e6d5466714fa\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"68ed0b460257ea77241316b54ff21eb643ff0bce3480c49bb201c14d5f6042fd\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a3f07ca5c82e7df5fd771be528d067d1e547adbaa46b6c75ca5762e7b0c71e7f\", \"acf9e7599f917f06541611a378977439abd33169d3b6c16e930054241e05c1b0\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"fc18bb99f3723a86c375e828c01d1d0bffddcdd2b37b04cbd21dc1b058716d7f\"]}, \"state_id\": \"04e3977b0ae520d1899120f3\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 66, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 3, 4, 6, 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\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"03cef0af6618e6d923161966\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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\": [1, 3, 4, 6, 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\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"03cef0af6618e6d923161966\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 67, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 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], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"034107851329b5499c1af3d2\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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\": [2, 3, 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], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"034107851329b5499c1af3d2\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 68, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 4, 7, 8, 9, 10, 11, 12, 13, 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\": 7, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6060606060606061, \"mean_separation\": 0.31616161616161614, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5928030303030303, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"19945cbc44f4d180dab68d07886335844930b281314c69ca5d53eeea411cbe48\", \"1b0afdfc8e17f0ff8147df740ad91f3c8bd45ff39f989057ff6a953bfb59610c\", \"2ac42cb9afcc2ee4f696938b56127b39655a2487421a6b481584e48108f9e29a\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"4b7065029bd41a5ea2c18191e7d076a2edc74738565a9f9b6c48ead7c12b741f\", \"5d0686b6e82b533708cf767106f66a625540a36b2b2fc9defac055df534b2993\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"87799aa0c3a1c5f2ddff705c372ac2e82213b4663a5c575bfdbbde0e0de668e1\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"ce7414fc423b7c1fe4626c289c80ea94ba7e53774b671375fa3036786d47e96d\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"ee65835b745d55ccec05013f0dff8ecd8d627bd23305c3498d17dc5aa98dd6af\"]}, \"state_id\": \"01023cd48bc14b91c3f3278e\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"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}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=0, 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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=0, 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\": [3, 4, 7, 8, 9, 10, 11, 12, 13, 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\": 7, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.6060606060606061, \"mean_separation\": 0.31616161616161614, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5928030303030303, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"19945cbc44f4d180dab68d07886335844930b281314c69ca5d53eeea411cbe48\", \"1b0afdfc8e17f0ff8147df740ad91f3c8bd45ff39f989057ff6a953bfb59610c\", \"2ac42cb9afcc2ee4f696938b56127b39655a2487421a6b481584e48108f9e29a\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"4b7065029bd41a5ea2c18191e7d076a2edc74738565a9f9b6c48ead7c12b741f\", \"5d0686b6e82b533708cf767106f66a625540a36b2b2fc9defac055df534b2993\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"87799aa0c3a1c5f2ddff705c372ac2e82213b4663a5c575bfdbbde0e0de668e1\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"ce7414fc423b7c1fe4626c289c80ea94ba7e53774b671375fa3036786d47e96d\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"ee65835b745d55ccec05013f0dff8ecd8d627bd23305c3498d17dc5aa98dd6af\"]}, \"state_id\": \"01023cd48bc14b91c3f3278e\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"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}}, {\"experiment_id\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 69, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4117647058823529, \"mean_separation\": 0.30392156862745096, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.45588235294117646, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"05fa8efbab66676a07313b03\", \"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\": 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\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, 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\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, 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\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4117647058823529, \"mean_separation\": 0.30392156862745096, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.45588235294117646, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"05fa8efbab66676a07313b03\", \"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\": 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\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 70, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"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, 27, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.16326530612244897, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1618fca29bdc15ce2ba5844b00d7b12fd7d67f30587f8a4a20e2be2c960fdb5f\", \"2225712c7cf50a7a15b78884d8b6a30303b4a40f009c0a262679dc6842b0c1b8\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"87706637b6b68c19e5de02ad43ca1fd551d6f28cd45a5566cb183012015a3063\", \"88057ca012d15a1b3cb20aa0650557381a8bee6a702fcf6ba4bf2634592c80cc\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"004876a6e9a4bc0ed343022e\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"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\": 1}}, {\"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\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, X3=0\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=1, Z2=1, 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=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, 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=0, 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=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=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, 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, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.16326530612244897, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1618fca29bdc15ce2ba5844b00d7b12fd7d67f30587f8a4a20e2be2c960fdb5f\", \"2225712c7cf50a7a15b78884d8b6a30303b4a40f009c0a262679dc6842b0c1b8\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"87706637b6b68c19e5de02ad43ca1fd551d6f28cd45a5566cb183012015a3063\", \"88057ca012d15a1b3cb20aa0650557381a8bee6a702fcf6ba4bf2634592c80cc\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"004876a6e9a4bc0ed343022e\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"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\": 1}}, {\"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\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 71, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.15238095238095237, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"034d8e6384999b6afb7c4db5\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\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, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.15238095238095237, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"034d8e6384999b6afb7c4db5\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 72, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4117647058823529, \"mean_separation\": 0.30392156862745096, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.45588235294117646, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"05f763981b1fb46137cbb549\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\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=1, 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\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\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=1, 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\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4117647058823529, \"mean_separation\": 0.30392156862745096, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.45588235294117646, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"05f763981b1fb46137cbb549\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 73, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.16326530612244897, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0145cb166d1c720a0d759ea8\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=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=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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\": [2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.16326530612244897, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0145cb166d1c720a0d759ea8\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 74, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.15238095238095237, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"04b23842bbf0a2e5860d3724\", \"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\": 0, \"Z1\": 1, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=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=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.15238095238095237, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"04b23842bbf0a2e5860d3724\", \"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\": 0, \"Z1\": 1, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 75, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 3, 5, 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, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.19607843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"05c234465671d38cb1667ebc\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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, 3, 5, 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, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.19607843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"05c234465671d38cb1667ebc\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 76, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 7, 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\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"022bed7e4cf31c377c30f7d0\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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 Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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\": [2, 3, 4, 5, 7, 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\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"022bed7e4cf31c377c30f7d0\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 77, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.30303030303030304, \"mean_separation\": 0.16161616161616163, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.30303030303030304, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"5b76549a2887f710af826f4e246770b098e9715399ef99f0d10b0564483beca8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"ba4005e87ac1f33b5fe86e3d5a1199b9556d721a292e697dd553053772cad73f\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"07186223db5c545f83a22be9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=1, 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\": [3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.30303030303030304, \"mean_separation\": 0.16161616161616163, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.30303030303030304, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"5b76549a2887f710af826f4e246770b098e9715399ef99f0d10b0564483beca8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"ba4005e87ac1f33b5fe86e3d5a1199b9556d721a292e697dd553053772cad73f\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"07186223db5c545f83a22be9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 78, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"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, 26, 27, 28, 29, 30, 31, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"0570234ebec54f56008f064f\", \"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\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\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=1, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=1, 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=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\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=0\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=1, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=1, 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=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\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, 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, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"0570234ebec54f56008f064f\", \"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\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 79, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"014153586c8095660539a9cd\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=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=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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\": [2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"014153586c8095660539a9cd\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 80, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5882352941176471, \"mean_separation\": 0.3325980392156863, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.6236213235294118, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0281afa2d3a1491cc70cf7a14499ea5fb331f493f089544cc890f4b761bd1882\", \"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"4af48d57b8d930ff14e85ff1ba0d1e457890550184134f896d83e7824da7b483\", \"53c1ded8258f8ad8f03ae5adede5c97ef79d6a8c482ce2c45010454b5568f0c8\", \"644dcd6b21ad3d7689513d78fc5c875176268f1cd5c64d3828824e9af397e386\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"acf9e7599f917f06541611a378977439abd33169d3b6c16e930054241e05c1b0\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c95d91f4951907da4d55ee7c64fa08cf341bb58d097a85456d4d8113815e00cc\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d538366004d463b25e070edcc9daed7c7846182ff43dfc8496f87cd6653fc10f\"]}, \"state_id\": \"04e9a8530c3116cdcb4c5637\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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\": [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], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5882352941176471, \"mean_separation\": 0.3325980392156863, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.6236213235294118, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0281afa2d3a1491cc70cf7a14499ea5fb331f493f089544cc890f4b761bd1882\", \"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"4af48d57b8d930ff14e85ff1ba0d1e457890550184134f896d83e7824da7b483\", \"53c1ded8258f8ad8f03ae5adede5c97ef79d6a8c482ce2c45010454b5568f0c8\", \"644dcd6b21ad3d7689513d78fc5c875176268f1cd5c64d3828824e9af397e386\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"acf9e7599f917f06541611a378977439abd33169d3b6c16e930054241e05c1b0\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c95d91f4951907da4d55ee7c64fa08cf341bb58d097a85456d4d8113815e00cc\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d538366004d463b25e070edcc9daed7c7846182ff43dfc8496f87cd6653fc10f\"]}, \"state_id\": \"04e9a8530c3116cdcb4c5637\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 81, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"00aecd2aab272e3fb14bb224\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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}}, {\"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\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=1\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\nExperiment 4:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=1\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\nExperiment 4:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"00aecd2aab272e3fb14bb224\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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}}, {\"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\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 82, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.1540816326530612, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.26964285714285713, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\"]}, \"state_id\": \"00866ba14afe6b87b60d17e4\", \"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\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: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=0, Y=1\n\nExperiment 5:\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: 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=0\n\nExperiment 3:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=0, Y=1\n\nExperiment 5:\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\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.1540816326530612, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.26964285714285713, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\"]}, \"state_id\": \"00866ba14afe6b87b60d17e4\", \"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 21, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 83, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5714285714285714, \"mean_separation\": 0.3233333333333333, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.60625, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"271a600afffc25fa364b4d73da68ffb14f509e125390d55f0df7fba9888d8417\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"722af11b4a8afb36318e3106265f608fe9efb994b74dbdf76491e49ce5cfced8\", \"7a3588d94c2aacf2b7d206a8e49dcfc5d163132d1bee706b33b98ac7e0da2368\", \"8759e77a412054da60d99948a16aefc28fff886721edb1a196fe5932350729e8\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"ee8d593aefdc3ef1ccb8912dfa87fef4bccfcc1c33dc4be313e297554203ec28\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\", \"f10df40a5e367a10c637eb1edcd2378322b6c5b1ae7fdd37676136dbed58547c\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"0728434036d5ad41e0aa3cb4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 36, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 1, \"Z1\": 0, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, 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=0\n\nExperiment 3:\n  inputs: X1=0, 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=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\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=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: set Z1=0\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=1, 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=0, Y=0\n\nExperiment 5:\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\": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5714285714285714, \"mean_separation\": 0.3233333333333333, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.60625, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"271a600afffc25fa364b4d73da68ffb14f509e125390d55f0df7fba9888d8417\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"722af11b4a8afb36318e3106265f608fe9efb994b74dbdf76491e49ce5cfced8\", \"7a3588d94c2aacf2b7d206a8e49dcfc5d163132d1bee706b33b98ac7e0da2368\", \"8759e77a412054da60d99948a16aefc28fff886721edb1a196fe5932350729e8\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"ee8d593aefdc3ef1ccb8912dfa87fef4bccfcc1c33dc4be313e297554203ec28\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\", \"f10df40a5e367a10c637eb1edcd2378322b6c5b1ae7fdd37676136dbed58547c\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"0728434036d5ad41e0aa3cb4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 84, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"03315888e53d1f472d0f9aa3\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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}}, {\"experiment_id\": 19, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, 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=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, 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=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"03315888e53d1f472d0f9aa3\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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}}, {\"experiment_id\": 19, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 85, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 9, 10, 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\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"017617401d3218f4f47ffca0\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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\": [2, 3, 4, 5, 6, 7, 9, 10, 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\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"017617401d3218f4f47ffca0\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 86, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 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, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5714285714285714, \"mean_separation\": 0.3078571428571429, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.577232142857143, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"097180c5d46dde7320c2132ba2fe66b2f6691e47281d5c31a9bc10d240453de6\", \"0e8433a007da914d4dd3706b2fa112ba29e3e432b3f7bdd88aa5534617dcb322\", \"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"63b4358687ce4e476ff6c76179c7b3118115d9efc69516f11fdb4a38c31fb9e1\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b75b74456a66a467dece872bcf09fa07729d9245745b1b3d77e7afb36dae1ad5\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d09333aacb954a8167feacc38d61b03691a718a5ac08c6b3a625eedb1d9a0c19\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"df7012bdb0df98e829080e2af4479f0350691243d155fc88b41b7d33bf65b627\", \"e7ab997b51f342440d32843d07bdcc8334d13ade74c3150f8f39e510eb3609fb\", \"f227066a8cde147900227e542588b00987177f854070c06903c4e7f634ee7484\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"02ccc5a1b15e205f6d641bd1\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"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}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, 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\nExperiment 5:\n  inputs: X1=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, 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\nExperiment 5:\n  inputs: X1=1, 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, 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, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5714285714285714, \"mean_separation\": 0.3078571428571429, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.577232142857143, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"097180c5d46dde7320c2132ba2fe66b2f6691e47281d5c31a9bc10d240453de6\", \"0e8433a007da914d4dd3706b2fa112ba29e3e432b3f7bdd88aa5534617dcb322\", \"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"63b4358687ce4e476ff6c76179c7b3118115d9efc69516f11fdb4a38c31fb9e1\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b75b74456a66a467dece872bcf09fa07729d9245745b1b3d77e7afb36dae1ad5\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d09333aacb954a8167feacc38d61b03691a718a5ac08c6b3a625eedb1d9a0c19\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"df7012bdb0df98e829080e2af4479f0350691243d155fc88b41b7d33bf65b627\", \"e7ab997b51f342440d32843d07bdcc8334d13ade74c3150f8f39e510eb3609fb\", \"f227066a8cde147900227e542588b00987177f854070c06903c4e7f634ee7484\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"02ccc5a1b15e205f6d641bd1\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"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}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 87, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 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\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"4b281f0c85cad823411a5d6de1bc6db816afdc489e91292954ff40507b2939da\", \"c3298509ca1b2c95a8f3198e58d2d27fddd21c3e91675bbb6fb18adf6a3478cd\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"05cc4dfc936107686f0e4885\", \"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\": 1, \"Z2\": 1}}, {\"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\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=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=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=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=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=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\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 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\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"4b281f0c85cad823411a5d6de1bc6db816afdc489e91292954ff40507b2939da\", \"c3298509ca1b2c95a8f3198e58d2d27fddd21c3e91675bbb6fb18adf6a3478cd\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"05cc4dfc936107686f0e4885\", \"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\": 1, \"Z2\": 1}}, {\"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\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 88, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.2285714285714286, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4000000000000001, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"014d605d704418355617a60b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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\": [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, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.2285714285714286, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4000000000000001, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"014d605d704418355617a60b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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\": 1, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 89, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5882352941176471, \"mean_separation\": 0.31176470588235294, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5845588235294118, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3c486722d9407e68035ba87d759fc64b111025943f92766c26056f072b997707\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"5e971b03487926c733eb64e19edfab27d6fc61cb039b7f33a3e56ab92361f0db\", \"722af11b4a8afb36318e3106265f608fe9efb994b74dbdf76491e49ce5cfced8\", \"7a3588d94c2aacf2b7d206a8e49dcfc5d163132d1bee706b33b98ac7e0da2368\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b621e7ee4e35557cfe78d22e025c530d5a9a5ef34f7f284a980fd96d5f02706c\", \"c4df8d03402f7120aeefed4b0d481409ea52d095a21470de2ce63c4ea2160fc1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"e9b854ddd7456c5c06746a2b3aa21749fb00f7aa1c2de23214c75fcd712a462f\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\", \"f2091f506c47c8b6157cb873c9de6ccc9f60d039fe8743930da6798424456d86\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"069d61d7bdb0905f4e7af770\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_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\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"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}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5882352941176471, \"mean_separation\": 0.31176470588235294, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5845588235294118, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3c486722d9407e68035ba87d759fc64b111025943f92766c26056f072b997707\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"5e971b03487926c733eb64e19edfab27d6fc61cb039b7f33a3e56ab92361f0db\", \"722af11b4a8afb36318e3106265f608fe9efb994b74dbdf76491e49ce5cfced8\", \"7a3588d94c2aacf2b7d206a8e49dcfc5d163132d1bee706b33b98ac7e0da2368\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b621e7ee4e35557cfe78d22e025c530d5a9a5ef34f7f284a980fd96d5f02706c\", \"c4df8d03402f7120aeefed4b0d481409ea52d095a21470de2ce63c4ea2160fc1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"e9b854ddd7456c5c06746a2b3aa21749fb00f7aa1c2de23214c75fcd712a462f\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\", \"f2091f506c47c8b6157cb873c9de6ccc9f60d039fe8743930da6798424456d86\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"069d61d7bdb0905f4e7af770\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_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\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 90, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 3, 4, 5, 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, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.19607843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"026b7ad391676e7a26795ae8\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"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\": 1, \"Z1\": 0, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: 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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, 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\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, 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\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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\": [1, 3, 4, 5, 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, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.19607843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"026b7ad391676e7a26795ae8\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"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\": 1, \"Z1\": 0, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 91, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 35, 36, 37, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.2285714285714286, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4000000000000001, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"00d28497721afa9571ab5820\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 38, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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\": [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, 35, 36, 37, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.2285714285714286, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4000000000000001, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"00d28497721afa9571ab5820\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 92, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5714285714285714, \"mean_separation\": 0.29357142857142854, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5504464285714286, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"6fbe144ec8c0329a10b573e1f9fae4346333bd3b37dccf1a121298f158eb419f\", \"722af11b4a8afb36318e3106265f608fe9efb994b74dbdf76491e49ce5cfced8\", \"7a3588d94c2aacf2b7d206a8e49dcfc5d163132d1bee706b33b98ac7e0da2368\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b621e7ee4e35557cfe78d22e025c530d5a9a5ef34f7f284a980fd96d5f02706c\", \"c4df8d03402f7120aeefed4b0d481409ea52d095a21470de2ce63c4ea2160fc1\", \"cc22710197819424f766df593d91c5b6dcbb76db63676b359457ba4cfda28900\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"e447453e1305f35b3e2536654ec22d22af23c2d4f34160000a1b3dccfb9c54b2\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\", \"f2091f506c47c8b6157cb873c9de6ccc9f60d039fe8743930da6798424456d86\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"0133fdf5e11736573382dadf\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 27, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\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 Z2=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\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: none\n  observed: Z1=0, Z2=1, 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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\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\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5714285714285714, \"mean_separation\": 0.29357142857142854, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5504464285714286, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"6fbe144ec8c0329a10b573e1f9fae4346333bd3b37dccf1a121298f158eb419f\", \"722af11b4a8afb36318e3106265f608fe9efb994b74dbdf76491e49ce5cfced8\", \"7a3588d94c2aacf2b7d206a8e49dcfc5d163132d1bee706b33b98ac7e0da2368\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b621e7ee4e35557cfe78d22e025c530d5a9a5ef34f7f284a980fd96d5f02706c\", \"c4df8d03402f7120aeefed4b0d481409ea52d095a21470de2ce63c4ea2160fc1\", \"cc22710197819424f766df593d91c5b6dcbb76db63676b359457ba4cfda28900\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"e447453e1305f35b3e2536654ec22d22af23c2d4f34160000a1b3dccfb9c54b2\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\", \"f2091f506c47c8b6157cb873c9de6ccc9f60d039fe8743930da6798424456d86\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"0133fdf5e11736573382dadf\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 93, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"02cec6aefd2b5dde9ad96a72\", \"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\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"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\": 0, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\n  intervention: set Z1=0\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=1, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\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: set Z1=0\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=1, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\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\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"02cec6aefd2b5dde9ad96a72\", \"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\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"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\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 94, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.2285714285714286, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4000000000000001, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"00e632acce725822f033a0bc\", \"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: 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=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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=1, Y=0\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=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.2285714285714286, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4000000000000001, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"00e632acce725822f033a0bc\", \"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 95, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 8, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3125, \"mean_separation\": 0.16666666666666666, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.3125, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"5b76549a2887f710af826f4e246770b098e9715399ef99f0d10b0564483beca8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"ba4005e87ac1f33b5fe86e3d5a1199b9556d721a292e697dd553053772cad73f\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"0216c12ea9b23fbf10aef3c9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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\": 19, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 8:\n  inputs: X1=1, X2=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 8:\n  inputs: X1=1, X2=1, 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\": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 8, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.3125, \"mean_separation\": 0.16666666666666666, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.3125, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"5b76549a2887f710af826f4e246770b098e9715399ef99f0d10b0564483beca8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"ba4005e87ac1f33b5fe86e3d5a1199b9556d721a292e697dd553053772cad73f\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"0216c12ea9b23fbf10aef3c9\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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\": 19, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 96, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 3, 4, 6, 7, 8, 9, 10, 11, 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\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"016edb8924a9b558f0da4c47\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 6:\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\": [1, 3, 4, 6, 7, 8, 9, 10, 11, 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\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"016edb8924a9b558f0da4c47\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 5, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 97, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5142857142857142, \"mean_separation\": 0.2857142857142857, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0249e6a592c620827b5e2604\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 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\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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: 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=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, 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=0, Z2=1, 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=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, 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, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.5142857142857142, \"mean_separation\": 0.2857142857142857, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c3876b7b4decdaadda51efa44fa6ac6ef1282f7b7d1495adadec3a8e3acc0a9a\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0249e6a592c620827b5e2604\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 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\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 98, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.42424242424242425, \"mean_separation\": 0.22626262626262628, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.42424242424242425, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"75dc7f7a00c7c6e3d54a0bdd03f5752e40ba48fdbd733c949510e80581d9dd6b\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f81316d3e2e3727a5b24e6bb253e0524f80f0e53b00bb878542220bf99f8e0b6\"]}, \"state_id\": \"03b368580039ccb3df71bf3b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, 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\": [3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.42424242424242425, \"mean_separation\": 0.22626262626262628, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.42424242424242425, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"75dc7f7a00c7c6e3d54a0bdd03f5752e40ba48fdbd733c949510e80581d9dd6b\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f81316d3e2e3727a5b24e6bb253e0524f80f0e53b00bb878542220bf99f8e0b6\"]}, \"state_id\": \"03b368580039ccb3df71bf3b\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 99, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 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, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.19607843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"04425092cd4f4aca68192d93\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"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\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, 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, 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, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.19607843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"04425092cd4f4aca68192d93\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"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\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 100, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.2285714285714286, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4000000000000001, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"01e657f51f0079264a5c96e8\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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=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=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.2285714285714286, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4000000000000001, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"01e657f51f0079264a5c96e8\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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\": 1, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 101, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.1568627450980392, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"5b76549a2887f710af826f4e246770b098e9715399ef99f0d10b0564483beca8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"ba4005e87ac1f33b5fe86e3d5a1199b9556d721a292e697dd553053772cad73f\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"019ba754348888e5d59802df\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"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\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, 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\": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.1568627450980392, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"5b76549a2887f710af826f4e246770b098e9715399ef99f0d10b0564483beca8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"ba4005e87ac1f33b5fe86e3d5a1199b9556d721a292e697dd553053772cad73f\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"019ba754348888e5d59802df\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"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\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 102, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"0324f58ea5d3eb6a5917968b\", \"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\": 19, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 23, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=1, 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=0, Z2=0, 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=0\n\nExperiment 5:\n  inputs: X1=1, 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: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=1, 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=0, Z2=0, 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=0\n\nExperiment 5:\n  inputs: X1=1, 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\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"0324f58ea5d3eb6a5917968b\", \"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\": 19, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 23, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"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\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 103, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1618fca29bdc15ce2ba5844b00d7b12fd7d67f30587f8a4a20e2be2c960fdb5f\", \"2225712c7cf50a7a15b78884d8b6a30303b4a40f009c0a262679dc6842b0c1b8\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"87706637b6b68c19e5de02ad43ca1fd551d6f28cd45a5566cb183012015a3063\", \"88057ca012d15a1b3cb20aa0650557381a8bee6a702fcf6ba4bf2634592c80cc\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"015a32aeafeec70c1b9ac1f8\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 1}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"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\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=1\n\nExperiment 6:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=1\n\nExperiment 6:\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\": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 24, 25, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1618fca29bdc15ce2ba5844b00d7b12fd7d67f30587f8a4a20e2be2c960fdb5f\", \"2225712c7cf50a7a15b78884d8b6a30303b4a40f009c0a262679dc6842b0c1b8\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"87706637b6b68c19e5de02ad43ca1fd551d6f28cd45a5566cb183012015a3063\", \"88057ca012d15a1b3cb20aa0650557381a8bee6a702fcf6ba4bf2634592c80cc\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"015a32aeafeec70c1b9ac1f8\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 1}}, {\"experiment_id\": 22, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"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\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 104, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.15238095238095237, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"04fe43bfcdf1b9c45805733a\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"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}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=1\n  observed: Z1=0, Z2=1, Y=0\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\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\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\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.15238095238095237, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"04fe43bfcdf1b9c45805733a\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"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}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 105, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"05488a31c7a2270e9d8c1d67\", \"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\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"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\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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 Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=1\n\nExperiment 6:\n  inputs: X1=1, 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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=1\n\nExperiment 6:\n  inputs: X1=1, 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, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"05488a31c7a2270e9d8c1d67\", \"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\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"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\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 106, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.30303030303030304, \"mean_separation\": 0.17316017316017315, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.30303030303030304, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0167d8f2d51cacafd134f7f4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\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\": [3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.30303030303030304, \"mean_separation\": 0.17316017316017315, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.30303030303030304, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0167d8f2d51cacafd134f7f4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 9, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 107, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 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], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5882352941176471, \"mean_separation\": 0.3325980392156863, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.6236213235294118, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0281afa2d3a1491cc70cf7a14499ea5fb331f493f089544cc890f4b761bd1882\", \"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"4af48d57b8d930ff14e85ff1ba0d1e457890550184134f896d83e7824da7b483\", \"53c1ded8258f8ad8f03ae5adede5c97ef79d6a8c482ce2c45010454b5568f0c8\", \"644dcd6b21ad3d7689513d78fc5c875176268f1cd5c64d3828824e9af397e386\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"acf9e7599f917f06541611a378977439abd33169d3b6c16e930054241e05c1b0\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c95d91f4951907da4d55ee7c64fa08cf341bb58d097a85456d4d8113815e00cc\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d538366004d463b25e070edcc9daed7c7846182ff43dfc8496f87cd6653fc10f\"]}, \"state_id\": \"013516623dac1cff94a90436\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=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=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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=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=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=0\n\nExperiment 4:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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\": [2, 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], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5882352941176471, \"mean_separation\": 0.3325980392156863, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.6236213235294118, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0281afa2d3a1491cc70cf7a14499ea5fb331f493f089544cc890f4b761bd1882\", \"12bd43ba50c350214c009696bcfa3ee874761ed4c2ba0fb100248165b23c5082\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"4af48d57b8d930ff14e85ff1ba0d1e457890550184134f896d83e7824da7b483\", \"53c1ded8258f8ad8f03ae5adede5c97ef79d6a8c482ce2c45010454b5568f0c8\", \"644dcd6b21ad3d7689513d78fc5c875176268f1cd5c64d3828824e9af397e386\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9f969da84c97357a157e9aaea65afd4e727f51e0b4376bb70ca6610fcfc23bc4\", \"acf9e7599f917f06541611a378977439abd33169d3b6c16e930054241e05c1b0\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"c95d91f4951907da4d55ee7c64fa08cf341bb58d097a85456d4d8113815e00cc\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d538366004d463b25e070edcc9daed7c7846182ff43dfc8496f87cd6653fc10f\"]}, \"state_id\": \"013516623dac1cff94a90436\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 108, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 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\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"040145644e4872f233b2f46a\", \"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\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 35, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\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=0\n\nExperiment 3:\n  inputs: X1=0, 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=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\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=0\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=0\n\nExperiment 3:\n  inputs: X1=0, 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=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\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, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 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\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"040145644e4872f233b2f46a\", \"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\": 11, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0, \"Z1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 109, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 5, 6, 7, 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\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"00b4b4abc5d576fa372fd0f0\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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\": [2, 3, 5, 6, 7, 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\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.16806722689075632, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2941176470588236, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"00b4b4abc5d576fa372fd0f0\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 110, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.1568627450980392, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"074029879773ecd6ff1eb1db\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 31, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, 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=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, 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=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.1568627450980392, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"074029879773ecd6ff1eb1db\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 31, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 111, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 8, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"050d1ad3a2fd463f930d6142\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 8:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 8:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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\": [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, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 8, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"050d1ad3a2fd463f930d6142\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 112, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.30303030303030304, \"mean_separation\": 0.17316017316017315, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.30303030303030304, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0173b19bfe1a707d87282578\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=0\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\": [3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.30303030303030304, \"mean_separation\": 0.17316017316017315, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.30303030303030304, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"0173b19bfe1a707d87282578\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 12, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 33, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 113, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 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\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.15238095238095237, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"02a76882108f0038e2b15bbb\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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 Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\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 Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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\": [2, 3, 4, 5, 6, 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\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.15238095238095237, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"02a76882108f0038e2b15bbb\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 114, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"012f55651042d5edf734db3f\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=1, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=1, X2=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, 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\nExperiment 5:\n  inputs: X1=1, 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=0, 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=0, X3=0\n  intervention: none\n  observed: Z1=0, Z2=0, 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\nExperiment 5:\n  inputs: X1=1, 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, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4, \"mean_separation\": 0.29523809523809524, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4428571428571429, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"012f55651042d5edf734db3f\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 20, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 115, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"0140d51914827d899055e253\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 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\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=1\n\nExperiment 6:\n  inputs: X1=1, X2=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=0, Y=1\n\nExperiment 6:\n  inputs: X1=1, X2=1, 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\": [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, 30, 31, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"0c5ab6a28e607245a896c1e86c0768914491b2b465f34a1ba87a6765bb066650\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f0717c7202969d7a97bb5c658eacf5cb493b50fa7f949c1f9602ece9713622c9\"]}, \"state_id\": \"0140d51914827d899055e253\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 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\": 32, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 116, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 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\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4117647058823529, \"mean_separation\": 0.24485294117647058, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.45909926470588236, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1c438e00730c2114ec43cd8a3618709f10db4f266582e982242ba3d43e07e549\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"04513bdd1c6a8f63a4d25902\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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\": [2, 3, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 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\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4117647058823529, \"mean_separation\": 0.24485294117647058, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.45909926470588236, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1c438e00730c2114ec43cd8a3618709f10db4f266582e982242ba3d43e07e549\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"04513bdd1c6a8f63a4d25902\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 1, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 117, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [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, 30, 31, 32, 33, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"01c28a8bd0e1ef4065c71db0\", \"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\": 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\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, 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=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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=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=0\n\nExperiment 3:\n  inputs: X1=0, 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=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=1\n  intervention: set Z2=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\": [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, 30, 31, 32, 33, 35, 36, 37, 38], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"01c28a8bd0e1ef4065c71db0\", \"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\": 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\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 39, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 1}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 118, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 6, 7, 8, 9, 11, 12, 13, 14, 15, 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\": 8, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4375, \"mean_separation\": 0.28236607142857145, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.49414062500000006, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"0255ec32e85876f82fabe210\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"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\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 8:\n  inputs: X1=1, X2=0, 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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 8:\n  inputs: X1=1, X2=0, 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\": [3, 6, 7, 8, 9, 11, 12, 13, 14, 15, 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\": 8, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4375, \"mean_separation\": 0.28236607142857145, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.49414062500000006, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"0255ec32e85876f82fabe210\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 10, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"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\": 28, \"inputs\": {\"X1\": 1, \"X2\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 119, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 5, 6, 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\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.1568627450980392, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"0531831b16c60875692142d4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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\": [2, 3, 5, 6, 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\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.1568627450980392, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1501f3558b8f81cb511aa560cd7ef490821442cb544855f7d76d152152761788\", \"33e64f46e161e15bf4d6b1b17901669249a8438af3d253b4e1cfddf48e5c0628\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"8f7f91c37cbe51374eaf7068f5cad78d91e2e090051180d170ab5097629b7a32\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"afd2164d41d2d9169e810f6816d5664ceda31aa1885c37f60077d8d4111e5793\", \"b5111b5f9b1e5098ba47dc81ed8df18fb21bf6be5c0a4a7fd992ef04da8c8528\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"d87489121eb018ac377bcdce3edab3ec9cd6326ad00acd0b693af34da3705660\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"0531831b16c60875692142d4\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 4, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 120, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 8, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"01bd7df1bf1e1ba4b0774a2a\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 1, \"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\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 8:\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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 8:\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\": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 31, 32, 33, 34, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 8, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.0, \"mean_separation\": 0.0, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.0, \"separation_bucket\": \"low\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\"]}, \"state_id\": \"01bd7df1bf1e1ba4b0774a2a\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 1, \"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\": 30, \"inputs\": {\"X1\": 1, \"X2\": 1, \"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\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 121, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4117647058823529, \"mean_separation\": 0.2857142857142857, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"7a3588d94c2aacf2b7d206a8e49dcfc5d163132d1bee706b33b98ac7e0da2368\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b75b74456a66a467dece872bcf09fa07729d9245745b1b3d77e7afb36dae1ad5\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"039eacc2c1ac223c28f4da70\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"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\": 1, \"Z1\": 0, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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\": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 37, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.4117647058823529, \"mean_separation\": 0.2857142857142857, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"7a3588d94c2aacf2b7d206a8e49dcfc5d163132d1bee706b33b98ac7e0da2368\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"b75b74456a66a467dece872bcf09fa07729d9245745b1b3d77e7afb36dae1ad5\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"039eacc2c1ac223c28f4da70\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"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\": 1, \"Z1\": 0, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 122, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.42424242424242425, \"mean_separation\": 0.22626262626262628, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.42424242424242425, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"75dc7f7a00c7c6e3d54a0bdd03f5752e40ba48fdbd733c949510e80581d9dd6b\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f81316d3e2e3727a5b24e6bb253e0524f80f0e53b00bb878542220bf99f8e0b6\"]}, \"state_id\": \"0012c7e19fb8a022168d445d\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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: none\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, 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=0, Z2=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, 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\": [3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 7, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.42424242424242425, \"mean_separation\": 0.22626262626262628, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.42424242424242425, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"3f018ec43405891dba3512288b15488d70fc4b92803146f1985e44085c5ccab2\", \"41df8031f186e0626457d7265c95a74ac337d99582eade19dd6efeebc00fb529\", \"538736a4b082875b65c7a2ec54c899d9799183095b5fd3d25c602a7ccfd3e19f\", \"75dc7f7a00c7c6e3d54a0bdd03f5752e40ba48fdbd733c949510e80581d9dd6b\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"b36b5bc846f99a77e0c77eb8df24611203b168db55fb1c5296acda7aa4bb4e79\", \"b79a69f910f4bbf7637d7fa9ef5019679a60eef5669da576611cb78ea82c8709\", \"bb86905f5f64146db4516c44616ed5cdcea1b1f23eec67b08248d668e7300d23\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"de9b66f6834b1b4a23c8836c06c5110c85eca72a1c6dc70c07213942ede1c03a\", \"f81316d3e2e3727a5b24e6bb253e0524f80f0e53b00bb878542220bf99f8e0b6\"]}, \"state_id\": \"0012c7e19fb8a022168d445d\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 6, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 1}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"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\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 123, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 4, 5, 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, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.19047619047619047, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"023244c5744eb5e3298b77ba\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=0, Z2=1, 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=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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=1, 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=0, Z2=0, Y=0\n\nExperiment 4:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 5, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.2857142857142857, \"mean_separation\": 0.19047619047619047, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.2857142857142857, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"023244c5744eb5e3298b77ba\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 124, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 8, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.375, \"mean_separation\": 0.2767857142857143, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.48437500000000006, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"efe12d59a775be045b823b4e74f28fec605d56b654deb15f0b36c7a9472758fc\"]}, \"state_id\": \"02cae857325d7c68aee4385c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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\": 0, \"Z1\": 1, \"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\": 23, \"inputs\": {\"X1\": 1, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 8:\n  inputs: X1=1, X2=0, 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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=0\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=0\n  observed: Z1=0, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\n  inputs: X1=0, X2=1, X3=0\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 7:\n  inputs: X1=1, X2=0, X3=0\n  intervention: set Z1=1\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 8:\n  inputs: X1=1, X2=0, 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\": [4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"metadata\": {\"evidence_size\": 8, \"family_bucket\": \"mixed\", \"maximum_separation\": 0.375, \"mean_separation\": 0.2767857142857143, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.48437500000000006, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"446480a0f6e3ece1a285220120598a7c6d8e37146cee005264068b5081fb05f8\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"9b8d27d4e54003b081c035cd096b97a4a0243ab38213536228de476479716fcb\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"efe12d59a775be045b823b4e74f28fec605d56b654deb15f0b36c7a9472758fc\"]}, \"state_id\": \"02cae857325d7c68aee4385c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 2, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"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\": 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\": 0, \"Z1\": 1, \"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\": 23, \"inputs\": {\"X1\": 1, \"X2\": 0, \"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 125, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 2, 3, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 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\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4, \"mean_separation\": 0.23785714285714288, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4459821428571429, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1c438e00730c2114ec43cd8a3618709f10db4f266582e982242ba3d43e07e549\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"0284f3476b0e8af4b51435a2\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=1, Y=0\n\nExperiment 2:\n  inputs: X1=0, X2=0, X3=0\n  intervention: set Z2=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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=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=1\n  observed: Z1=0, Z2=1, Y=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=1\n  intervention: set Z1=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 5:\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\": [1, 2, 3, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 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\": 5, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.4, \"mean_separation\": 0.23785714285714288, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.4459821428571429, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 16}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"1c438e00730c2114ec43cd8a3618709f10db4f266582e982242ba3d43e07e549\", \"1ee2f2e6394f308de5919dd5b00420c18d74cb71c0aa9eb96f0c39714fb07634\", \"325f6d64050049a952d0d356cd708d716469620621ac520488db408cf4e68be8\", \"3674b557f32e49854825d3e6a352ed2f89163a11ad0ca3220fde2a35979fd6b2\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"46c15e618ecd7be92a4e0571deb8d0fa01e1aaf6001108a62243649984c3f2e8\", \"49eebeef2171c95a3ca589f3033fe6ca47ba6034b711705e439e8b582bdff59e\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"8a3f3d7ee29e72d8641415f9de2734210e7de60c936d044e852865c3445bc20a\", \"9c6f88852c2770107b4b64ca0e0e154859ea1b0d1270a20079680054b13b93f1\", \"a12d6ead22cbb1a60fa4fb35373861894c5f34633de8c96c4bb1d6ce68f1eecc\", \"c448f30c1016619ee05fdda1d65f42fe285a95bc39fc95dc0301512f9eecdb12\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"dace77701fc03926ae3f2694a47cbc25db1a9e049e2c309cec20548cdbfcf9d8\", \"e20102007ab76aa0d99f8e6a507e1e16565f4c76ba1755d6c349c3c92e9ce65a\", \"e9e6b07ac1c36130046ccbd1038fea397e1307a56565b5670b93281534bfe8e7\"]}, \"state_id\": \"0284f3476b0e8af4b51435a2\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"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\": 1, \"Z2\": 1}}, {\"experiment_id\": 17, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 1, \"Z1\": 1, \"Z2\": 0}}]}"}
{"agent_name": "causal_micro_lab_agent_loop", "data_source": "causal_micro_lab_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 126, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [1, 3, 4, 5, 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, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.19607843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"02c213f27eb5d88d67969f58\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"experiment_id\": 37, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 1}, \"intervention\": \"DO_Z1_1\", \"observation\": {\"Y\": 1, \"Z1\": 1, \"Z2\": 0}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, 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\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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=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=0\n\nExperiment 3:\n  inputs: X1=0, X2=0, X3=1\n  intervention: set Z2=1\n  observed: Z1=1, 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\nExperiment 5:\n  inputs: X1=1, X2=1, X3=0\n  intervention: set Z2=1\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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, 3, 4, 5, 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, 35, 36, 38, 39], \"metadata\": {\"evidence_size\": 6, \"family_bucket\": \"within_family\", \"maximum_separation\": 0.29411764705882354, \"mean_separation\": 0.19607843137254902, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.29411764705882354, \"separation_bucket\": \"medium\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 4}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"342143f1b5907b191f5da705b935db78627ea401304cfb54771bbef36a4ace81\", \"3a099a698aef9c7630b55036b74a0e9bafbaf5d2ccf7da9408676ac8c9877d4a\", \"c264bc880ad6a4902ee2247ff33c031f5c91d68523a2b8c5e15da9bfaa52a0e0\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\"]}, \"state_id\": \"02c213f27eb5d88d67969f58\", \"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\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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\": 0, \"Z1\": 0, \"Z2\": 0}}, {\"experiment_id\": 34, \"inputs\": {\"X1\": 1, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_1\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 1}}, {\"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_val", "env_spec_json": "{\"agent\": {\"length_penalty_max\": -0.2, \"length_penalty_start\": 3072.0, \"mask_truncated\": true}, \"env_type\": \"causal_micro_lab\", \"max_commit\": 1, \"max_consecutive_invalid\": 2, \"max_steps\": 1, \"protocol\": \"single\", \"seed\": 127, \"task\": {\"evidence_consistent_reward\": 0.0, \"nonempty_output_reward\": 0.0, \"parse_valid_reward\": 0.0, \"rule_marker_reward\": 0.0, \"state\": {\"available_experiment_ids\": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 14, 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\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5882352941176471, \"mean_separation\": 0.31197478991596633, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5459558823529411, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3c486722d9407e68035ba87d759fc64b111025943f92766c26056f072b997707\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"cc22710197819424f766df593d91c5b6dcbb76db63676b359457ba4cfda28900\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"037606e768c78286ae00116c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0, \"Z2\": 1}}]}, \"syntax_valid_reward\": 0.2, \"valid_hypothesis_reward\": 1.0}}", "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=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=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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=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=0\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=1, Z2=1, Y=0\n\nExperiment 4:\n  inputs: X1=0, X2=1, X3=0\n  intervention: set Z2=0\n  observed: Z1=1, Z2=0, Y=0\n\nExperiment 5:\n  inputs: X1=0, X2=1, X3=1\n  intervention: none\n  observed: Z1=1, Z2=1, Y=0\n\nExperiment 6:\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\": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 14, 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\": 6, \"family_bucket\": \"cross_family\", \"maximum_separation\": 0.5882352941176471, \"mean_separation\": 0.31197478991596633, \"minimum_separation\": 0.0, \"normalized_mean_separation\": 0.5459558823529411, \"separation_bucket\": \"high\", \"separation_definition\": \"predictive_target_disagreement_v2\", \"separation_targets\": [\"Y\"], \"valid_mode_count\": 8}, \"private\": {\"hidden_mode_id\": \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"valid_mode_ids\": [\"3c486722d9407e68035ba87d759fc64b111025943f92766c26056f072b997707\", \"56866381db18dd12e827581aa04161d471d0033d67c664e21bf68d6fd06633f3\", \"7ed288292baf431792635fdc4b4229e55cf5d827edf4a6ffda0d845e2575d1b8\", \"802d05ef1f20ad1e0d923d0ea4a2d859bc12cdeb36f11590f15294adbc1dff0f\", \"99d547fd8d90d364f92473165e8201f73ac1af14186ca6ed3977216bc7332b93\", \"cc22710197819424f766df593d91c5b6dcbb76db63676b359457ba4cfda28900\", \"d426363a002681ff4034d9a81c270d2660a3fa8ab8d4df02b16dc13ca2338608\", \"f75c641964e91c137e7c1dd1ca7244e9ca96f64519511024025e61c8f74dfe31\"]}, \"state_id\": \"037606e768c78286ae00116c\", \"visible_experiments\": [{\"experiment_id\": 0, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"OBSERVE\", \"observation\": {\"Y\": 0, \"Z1\": 0, \"Z2\": 1}}, {\"experiment_id\": 1, \"inputs\": {\"X1\": 0, \"X2\": 0, \"X3\": 0}, \"intervention\": \"DO_Z1_0\", \"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\": 1}}, {\"experiment_id\": 13, \"inputs\": {\"X1\": 0, \"X2\": 1, \"X3\": 0}, \"intervention\": \"DO_Z2_0\", \"observation\": {\"Y\": 0, \"Z1\": 1, \"Z2\": 0}}, {\"experiment_id\": 15, \"inputs\": {\"X1\": 0, \"X2\": 1, \"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\": 0, \"Z2\": 1}}]}"}