{ "schema_version": "image_classification_explanation_audit_v1", "created_utc": "2026-10-15T09:44:24.187737+00:00", "protocol_sha256": "736096da318220195b03c1c62d21a07e89fef5c86b06e5079d40bc32ffe79a2c", "evaluation_results_sha256": "0d2147581189c3aedd6bd3f2b4eaa2bc89ed333121df4eaa73e9bc90e51d60f0", "control_audit_sha256": "d1fd5a0bb450c5fe0a4274a96109b423e3c425b277520b9c1777de2ac1be05ba", "reviewed_public_response_count": 45, "review_policy": "Every listed second-round main and permutation public answer was independently read in full. Only public content/explanation were displayed; hidden reasoning was not inspected. First-round audit remains unchanged.", "scoring_note": "A supported mechanism is consistent with the code and measured controls at its stated certainty; it is not necessarily exclusive. Ranking correctness is scored separately from mechanism validity, pair coverage and internal answer consistency.", "pending_public_responses": 0, "records": [ { "question_id": "q_ec5ca80d9651d097f461319ad3cd14b3", "model": "gpt-6-luna", "ranking_correct": true, "summary_zh": "识别网格直接读出与坐标交互的学习成本，明确把排序限制在300步预算，和两条确认差异一致。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "supported", "causal_calibration": "supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "A's preserved spatial structure and easier optimization are expected to win under only 300 updates", "judgment": "supported", "assessment": "空间版完整train/test CE低于坐标mean，符合有限预算易学读出解释，但没有单独测量中心矩形成。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "its coordinate channels allow the network to encode position-dependent moments", "judgment": "supported", "assessment": "正确的潜在计算路径；实际神经表示没有被直接辨识，不能从总效果认定已学出精确中心矩。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "8506cc0e1499e263390681099ee3eeb8a2448b174fc6fd0977139bcb8b989ec2", "probe": null, "instance_id": 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"answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "giving the classifier coarse location-specific features from both channels", "judgment": "supported", "assessment": "3×3网格确实保留向线性头提供的位置索引。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "its global average pooling encourages translation invariance and can discard the location information", "judgment": "supported", "assessment": "使用encourages/can而非严格不变定理；padding和局部关系线索仍存在，实际mean完整train/test近chance。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "5d07324c3bfc2134b0520725ae0a76e6d1e5453798de7d84e7391d72b5f61c62", "probe": null, "instance_id": "pooling_coordinate_s841902", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "mean": { "node_id": "candidate_c93709ee26aacbc9f437", "mean_train_ce": 1.3854690231382847, "mean_test_ce": 1.385668184608221 }, "coordinate_mean": { "node_id": "candidate_d91d654be99bc328c040", "mean_train_ce": 0.331308702530805, "mean_test_ce": 0.34536808996926993 }, "spatial": { "node_id": "candidate_f9bdfa11ef52bc5e3528", 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"592308099e4183b6f378d4cd52001e81bd14e927975abd309b49cf7aab204289", "probe": { "type": "letter_permutation", "source_question_id": "q_8a36fc443f07ff9476da1cb066a63d67" }, "instance_id": "pooling_coordinate_s841902", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "mean": { "node_id": "candidate_c93709ee26aacbc9f437", "mean_train_ce": 1.3854690231382847, "mean_test_ce": 1.385668184608221 }, "coordinate_mean": { "node_id": "candidate_d91d654be99bc328c040", "mean_train_ce": 0.331308702530805, "mean_test_ce": 0.34536808996926993 }, "spatial": { "node_id": "candidate_f9bdfa11ef52bc5e3528", "mean_train_ce": 0.025847295970606865, "mean_test_ce": 0.037714587822847534 }, "coordinate_spatial": { "node_id": "candidate_a644642ee0d58a7cbf09", "mean_train_ce": 0.10882683973613894, "mean_test_ce": 0.13160173430514988 } } }, { "question_id": "q_89167763187b9bba108b563ae46fedb6", "model": "gpt-6-luna", "ranking_correct": false, "summary_zh": "承认局部交互和边界线索，但以连续坐标的潜在精度推断其训练优势；这与固定预算结果相反。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "leaving only weak signals from local object interactions and boundary effects", "judgment": "supported", "assessment": "准确保留mean CNN的弱关系路径，避免把全局平均误作严格无信息。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "this is likely more effective for the continuously varying object positions", "judgment": "plausible_unmeasured", "assessment": "表示细致度不推出300步更低CE；空间版实际完整train/test更低。正文有条件化，但未量化坐标交互的学习成本。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "8bdb30b748154fa41a524eaf251bae4c57b1eae69a4535f8f4020441bc7cebde", "probe": { "type": "letter_permutation", "source_question_id": "q_ededc5c5112d4fee539b6d60dbb9549c" }, "instance_id": "pooling_coordinate_s841903", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "mean": { "node_id": "candidate_07cf9b9e2ef0417e1afc", "mean_train_ce": 1.3868277808651328, "mean_test_ce": 1.3867692332714796 }, "coordinate_mean": { "node_id": "candidate_c65bf21470c8b1e81842", "mean_train_ce": 0.29576607677154243, "mean_test_ce": 0.3118247539969161 }, "spatial": { "node_id": "candidate_d6f536a09652df88c280", "mean_train_ce": 0.0428850017970035, "mean_test_ce": 0.05138279516904731 }, "coordinate_spatial": { "node_id": "candidate_4b043b424317b46e875a", "mean_train_ce": 0.09338780207690434, "mean_test_ce": 0.11225657083559781 } } }, { "question_id": "q_fcf232651387ce984a8140da96c318d3", "model": "gpt-6-luna", "ranking_correct": false, "summary_zh": "主要按保留分辨率排序，遗漏stride2候选的5×5 block变化；对patch丢方向的风险没有独立实测支持。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "partly_supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "the main difference is how much spatial detail their convolutional stem preserves", "judgment": "incomplete", "assessment": "主比较还改变block kernel3/5，因此不是纯stem分辨率比较；解释没有说明较大kernel的具体作用。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "making the higher-frequency textures more vulnerable to aliasing or loss of orientation cues", "judgment": "plausible_unmeasured", "assessment": "学习式多通道4×4投影不等于标量抽点；stride4实际train/test近零，不能把潜在混叠风险当成本批主因。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ 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"mean_train_ce": 1.387207434978336, "mean_test_ce": 1.387269290164113 }, "k3_s2": { "node_id": "candidate_a434611033b8affdc6bd", "mean_train_ce": 0.4361275098086157, "mean_test_ce": 0.43677199679660994 }, "k5_s4": { "node_id": "candidate_4199c0cb23d1c4c4703a", "mean_train_ce": 7.223267036296499e-06, "mean_test_ce": 0.00011057178539647383 }, "patch4_learnability_anchor": { "node_id": "candidate_fad13efd0f5a1af0c4eb", "mean_train_ce": 2.318241267929011e-05, "mean_test_ce": 0.0238547362399828 } } }, { "question_id": "q_a5f2d1e9542f2df31803626953d1af76", "model": "gpt-6-luna", "ranking_correct": false, "summary_zh": "识别低LR欠学习与高LR风险，但默认0.001优先的预测不符合实际训练/测试；结尾诚实标为估计。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "0.0001 may make progress too slowly within the fixed budget", "judgment": "supported", "assessment": "低LR完整训练与测试CE均接近1.385，支持预算内欠学习。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "0.01 is a large constant rate for this setup and may cause overshooting", "judgment": "plausible_unmeasured", "assessment": "风险可能存在，但0.01实际完整train/test更低；没有独立轨迹/校准测量证明该风险主导。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "8b2a3631abc98e63370e3f291ba2ecf7e2c43ca693d07c9cd99e0987ae594f01", "probe": null, "instance_id": "learning_rate_s841901", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "lr1e4": { "node_id": "candidate_31abb7f5ae858e9f35e8", "mean_train_ce": 1.3848290643654764, "mean_test_ce": 1.3849183171987534 }, "lr1e3": { "node_id": "candidate_6ec59d4186f9b42d6754", "mean_train_ce": 0.5936531231127447, "mean_test_ce": 0.6058920508075971 }, "lr1e2": { "node_id": "candidate_b07779969f06e2a919a4", "mean_train_ce": 0.2570996006147652, "mean_test_ce": 0.2773895292477846 } } }, { "question_id": "q_ba675b4af4aaa388d5e0ecb29edac798", "model": "gpt-6-luna", "ranking_correct": false, "summary_zh": 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overshooting or unstable late training", "judgment": "plausible_unmeasured", "assessment": "确认数据中0.01反而取得最低train/test CE；没有参数轨迹、校准或局部曲率数据支持其作为本次主导因素。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "e66b14c82c3256ec892acfd0b2fbf91ee6ea5e4fa26d5e931bbafcf6eec8ebff", "probe": null, "instance_id": "learning_rate_s841902", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "lr1e4": { "node_id": "candidate_68c3418242c7dd2f2c5e", "mean_train_ce": 1.3849177029915154, "mean_test_ce": 1.3846740862354636 }, "lr1e3": { "node_id": 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"data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "the predicted ordering remains C, B, A", "judgment": "inconsistent", "assessment": "解释前文称C最高loss且B优于A，answer也提交B,A,C；此末句在同一最佳到最差约定下与两者不一致。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "82c2b90fa16971d22501c2cd3f1fc73f18fc431514e301a32afa3acd801ecd97", "probe": { "type": "letter_permutation", "source_question_id": "q_ba675b4af4aaa388d5e0ecb29edac798" }, "instance_id": "learning_rate_s841902", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "lr1e4": { "node_id": "candidate_68c3418242c7dd2f2c5e", "mean_train_ce": 1.3849177029915154, "mean_test_ce": 1.3846740862354636 }, "lr1e3": { "node_id": "candidate_3e9dff76e192bdb82479", "mean_train_ce": 0.43976016309534316, "mean_test_ce": 0.4430525932548335 }, "lr1e2": { "node_id": "candidate_878848a639e130ad11ad", "mean_train_ce": 0.11298999113532382, "mean_test_ce": 0.11492065699945897 } } }, { "question_id": "q_ec5ca80d9651d097f461319ad3cd14b3", "model": "gpt-6.1-sol", "ranking_correct": true, "summary_zh": "正确计算感受野并承认局部重叠和边界；将空间优势限制在短预算易学性，机制与控制一致。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "supported", "causal_calibration": "supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "overlap interactions and boundary effects offer only partial substitutes", "judgment": "supported", "assessment": "有限图像和局部交互的替代信息路径被保留，没有声称严格无关系信息。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "With only 300 updates, I expect A’s more direct spatial representation to yield lower cross-entropy", "judgment": "supported", "assessment": "完整train/test同时低于坐标mean，支持此有限优化解释但不是唯一机制证明。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "19a8c4b1c276d95939fd6c0fe6035ed2ecad522a7234592b74fc2e4e43428f40", "probe": null, "instance_id": "pooling_coordinate_s841901", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "mean": { "node_id": "candidate_3cbaed19cf1f1bd78892", "mean_train_ce": 1.387246625032276, "mean_test_ce": 1.3874168954789639 }, "coordinate_mean": { "node_id": "candidate_fe248f3711cecdf66c13", "mean_train_ce": 0.32638712151674554, "mean_test_ce": 0.33372093125944957 }, "spatial": { "node_id": "candidate_675b0f0945a65c7728d6", "mean_train_ce": 0.03477928249594697, "mean_test_ce": 0.025694066469441168 }, "coordinate_spatial": { "node_id": "candidate_4e616725a8ecc13c433b", "mean_train_ce": 0.11016854794434039, "mean_test_ce": 0.11184107999724802 } } }, { "question_id": "q_d26f5adafb3e08bb7f45a0b9d0d21acd", "model": "gpt-6.1-sol", "ranking_correct": true, "summary_zh": "重排后的解释与排序一致；区分坐标可表达更精细规则和本预算较慢学成的风险。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "supported", "causal_calibration": "supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "I expect this to converge less efficiently", "judgment": "supported", "assessment": "坐标mean完整train CE较高，与300步内学习效率的解释相容；未直接测得具体乘积中心矩。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "overlapping objects can provide useful local directional cues", "judgment": "supported", "assessment": "正确指出局部感受野仍可能获得关系信号，避免全局平均必然chance的推断。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "b4a817508c4c0827f65218fdc9fed6535f0f7027ae6b3fe36c4c729ed90d7002", "probe": { "type": "letter_permutation", "source_question_id": 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"supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "its local convolutions can only infer the relation indirectly through local interactions", "judgment": "supported", "assessment": "有限局部交互确实是无坐标mean的一条路径；当前train/test近chance只说明预算内未学好。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "B avoids requiring the shallow network and linear head to reconstruct relative comparisons from globally pooled coordinate features", "judgment": "supported", "assessment": "网格直接读出与坐标交互成本的解释符合完整train/test差异；不能据此唯一确认学得表示。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "916b8a980ec21ca03c519b109271efe88d2352bcb4ef4bd34bd88408a102f890", "probe": null, "instance_id": "pooling_coordinate_s841902", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "mean": { "node_id": "candidate_c93709ee26aacbc9f437", "mean_train_ce": 1.3854690231382847, "mean_test_ce": 1.385668184608221 }, "coordinate_mean": { "node_id": "candidate_d91d654be99bc328c040", "mean_train_ce": 0.331308702530805, "mean_test_ce": 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"data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "I favor C under the short, fixed training budget", "judgment": "supported", "assessment": "空间版完整train/test更低，与有限预算解释一致；此判断没有被扩张到无限训练或自然图像。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": 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"data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "B could rival or exceed C because its spatial grid is a more direct positional representation", "judgment": "supported", "assessment": "其列出的替代解释与确认结果一致；跨回答变化另列为permutation不稳定，不把本条正文视为自相矛盾。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", 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"mean_train_ce": 2.318241267929011e-05, "mean_test_ce": 0.0238547362399828 } } }, { "question_id": "q_9d3f74e3a6db4ef85ae57f643c6a4f29", "model": "gpt-6.1-sol", "ranking_correct": false, "summary_zh": "重排后仍以较细采样为优势；支持范围计算正确，但patch相位敏感性与IID排序的联系缺少证据。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "approximately 18×18-pixel receptive field", "judgment": "supported", "assessment": "核2/stride2 stem加两层k5给18像素理论支持；与另一候选20像素可比较。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], 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"additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "should reach the best combination of convergence and calibration", "judgment": "unsupported_calibration", "assessment": "没有独立校准评价且0.01 train/test最低；常用默认值不等于该数据/预算下已验证最佳。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], 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"q_fcf232651387ce984a8140da96c318d3", "model": "claude-opus-5-5", "ranking_correct": false, "summary_zh": "本次加入LayerNorm幅值假说并正确计算stem输入维度，但未独立证实该机制；仍错估stride2优于stride4。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "The 4×4 patch embedding is a learned linear map from 32 values to 24 channels", "judgment": "supported", "assessment": "两输入通道×16像素确为32维；说明patch不是标量抽点，输出24维未必丢失四类方向。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "The pre-norm LayerNorm then removes most of that pixel's magnitude information", "judgment": "plausible_unmeasured", "assessment": "LayerNorm作用于仿射stem之后的通道，偏置和混合可能保留幅值线索；尚未测表示或完成独立幅值/LN鉴别，不能把弱CNN结果唯一归于此。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "dc93b5e6f804cf34064bcb171afeff637aeb906efe6d01f5ba3511f9a6191339", "probe": null, "instance_id": "kernel_stride_texture_s841901", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { 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"summary_zh": "能正确指出面积相关亮度线索和欠学习，但将默认LR称已调好，并给未测参数位移近似赋予过强解释力。", "scores": { "code_fidelity": "partly_supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "per-pixel brightness still varies with shape area", "judgment": "supported", "assessment": "peak/area/energy基线确有约40–45%准确率，质量归一化未消除这些信息。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "C's weights move only about 0.1 in total", "judgment": 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"data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "Adam at 1e-4 moves parameters only about 0.1 in total per coordinate", "judgment": "unsupported_quantification", "assessment": "该量未从候选状态测量，自适应矩比和衰减使lr×steps不能充当一般位移估计；小LR欠学习应依据完整train CE。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/explanation_supporting_checks.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "918a5e53faabb7652c77638a511722a12e8a47a252a813a7e3c2dbdc2d26f5c2", "probe": { "type": "letter_permutation", "source_question_id": "q_a5f2d1e9542f2df31803626953d1af76" }, "instance_id": "learning_rate_s841901", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "lr1e4": { "node_id": "candidate_31abb7f5ae858e9f35e8", "mean_train_ce": 1.3848290643654764, "mean_test_ce": 1.3849183171987534 }, "lr1e3": { "node_id": "candidate_6ec59d4186f9b42d6754", "mean_train_ce": 0.5936531231127447, "mean_test_ce": 0.6058920508075971 }, "lr1e2": { "node_id": "candidate_b07779969f06e2a919a4", "mean_train_ce": 0.2570996006147652, 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"verbatim_public_quote": "Coordinate-augmented mean pooling only encodes position indirectly", "judgment": "supported", "assessment": "坐标依赖特征需在平均前形成；当前train/test差异支持这条有限预算解释，但不能唯一辨认中心矩。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "8e3f1dbe2e3b95419e29c5038da2c92a1eaebf7bd1e85854ad754fd0991667ff", "probe": null, "instance_id": "pooling_coordinate_s841901", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "mean": { "node_id": "candidate_3cbaed19cf1f1bd78892", "mean_train_ce": 1.387246625032276, "mean_test_ce": 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"data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "1da4537f24c159628c9341f9fa141c411191b677700cfc011715d03d910124ef", "probe": { "type": "letter_permutation", "source_question_id": "q_ec5ca80d9651d097f461319ad3cd14b3" }, "instance_id": "pooling_coordinate_s841901", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "mean": { "node_id": "candidate_3cbaed19cf1f1bd78892", "mean_train_ce": 1.387246625032276, "mean_test_ce": 1.3874168954789639 }, "coordinate_mean": { "node_id": "candidate_fe248f3711cecdf66c13", "mean_train_ce": 0.32638712151674554, "mean_test_ce": 0.33372093125944957 }, "spatial": { "node_id": "candidate_675b0f0945a65c7728d6", "mean_train_ce": 0.03477928249594697, "mean_test_ce": 0.025694066469441168 }, "coordinate_spatial": { "node_id": 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"data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "the readout is still the bottleneck", "judgment": "overstated", "assessment": "现有终点和控制支持readout干预总效果，但未唯一分离读出表达限制、中心矩学习成本与其他优化因素。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" } ], "response_sha256": "858f49532c1245623897d9c6edd7379d1828bcac13b05de3bdd48cc06516bbbf", "probe": null, "instance_id": "pooling_coordinate_s841902", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "mean": { "node_id": "candidate_c93709ee26aacbc9f437", "mean_train_ce": 1.3854690231382847, "mean_test_ce": 1.385668184608221 }, "coordinate_mean": { "node_id": "candidate_d91d654be99bc328c040", "mean_train_ce": 0.331308702530805, "mean_test_ce": 0.34536808996926993 }, "spatial": { "node_id": "candidate_f9bdfa11ef52bc5e3528", "mean_train_ce": 0.025847295970606865, "mean_test_ce": 0.037714587822847534 }, "coordinate_spatial": { "node_id": "candidate_a644642ee0d58a7cbf09", "mean_train_ce": 0.10882683973613894, "mean_test_ce": 0.13160173430514988 } } }, { "question_id": "q_a1af9439f07fdde610fbdc50eea762d9", "model": "claude-haiku-5-5", "ranking_correct": true, "summary_zh": "预测正确，但绝对位置“必需”和无位置信息说得过满；全体near-chance只是未实现的预测而非实测事实。", "scores": { "code_fidelity": "partly_supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "requires comparing the two objects' absolute locations", "judgment": "overstated", "assessment": "标签只需相对位移；绝对位置是可行计算路径，不是普遍必要条件，局部联合关系也可提供信号。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/explanation_supporting_checks.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "nothing gives the network absolute position", "judgment": "overstated", "assessment": "有限padding可提供边界线索，局部关系不必先恢复绝对坐标；实际弱训练不证明无信息。", "evidence_path": 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{ "verbatim_public_quote": "Stride-1 (C) has more spatial compute, which does not change parameter count", "judgment": "incorrect", "assessment": "本实现stem kernel=stride，因此改变stride也改变stem权重；对k3的stride4与stride1不能说参数数不变。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "the more compute-heavy model is likely to fit better in the same number of updates", "judgment": "plausible_unmeasured", "assessment": "算量更大不必然带来更多有效优化信号；实际stride1 train/test≈1.387，而stride4近零。多通道patch投影也不等同于单通道Nyquist抽点。", "evidence_path": 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"supported" }, "annotations": [ { "verbatim_public_quote": "the same initial weights per seed", "judgment": "incorrect", "assessment": "不同stem/kernel改变state形状与初始化；实际跨候选initial_state hash不匹配，只有batch stream配对。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/quality_analysis/classification_quality.json" ], "scope_limit": "当前固定合成数据、候选与有限训练预算。局部控制为描述性非排他机制证据；排序正确不证明解释正确，错误排序也不自动反证有保留的机制假说。" }, { "verbatim_public_quote": "the finest resolution among the three", "judgment": "incorrect", "assessment": "stride1候选A为24×24，stride2 base B为12×12；B不是最细网格。正文末尾又写3×3RF，实际为5×5。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_v1_20261015/classification/control_audit.json", "additional_evidence_paths": [ 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