{ "schema_version": "image_classification_explanation_audit_v1", "created_utc": "2026-10-15T10:24:27.883014+00:00", "protocol_sha256": "3d4e945409f8dd8bd0745881c38354b6aec88b51a12b06b2618b1133e6e90e90", "evaluation_results_sha256": "d37f38466fc6746640dbca5571aff8b1d496cf54f1bf536c1074c2d6f8a1c3bb", "control_audit_sha256": "1cbd0293b5a0852a689f2a9e02508867b21cad81188aec89de15e2d81e52ddcf", "supporting_checks_sha256": "b9f1c2009858331eb6a4b5cb645cae0781d0d7dc300e46b5d62168c28d81e7a5", "component_sha256": { "explanation_audit_operator_review.json": "f8e7f53a2de07c09ae228233c37f4a9b8069cc25e335cab1cd19b77d119bccf3", "explanation_audit_patch_review.json": "6f7cac5bcaa8f447a2147124d64f80df9eb30ba64de9aa48573effa240332169" }, "reviewed_public_response_count": 64, "public_response_missing_count": 8, "pending_public_responses": 0, "review_policy": "Each completed public main and permutation answer independently read in full under the pre-frozen rubric. Only public content/explanation displayed; hidden reasoning not inspected. Operator and patch reviews independently assigned and merged with exact response/quote bindings. No APIs invoked by reviewers.", "scoring_note": "Supported means consistent with code and measured evidence at the stated certainty, not unique causal proof. Wrong ranking does not automatically negate a hedged mechanism hypothesis; correct ranking does not validate inaccurate code, impossibility or optimization-bound claims. 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"evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" }, { "verbatim_public_quote": "making reliable learning less likely in 75 updates", "judgment": "plausible_unmeasured", "assessment": "参数多不必然短训更慢；实际MLP在75步train/test均最低，不能将通用图像先验直接等同本任务易学性。", "evidence_path": 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"model": "gpt-6-luna", "ranking_correct": false, "summary_zh": "识别MLP直接像素路径且判断其胜过ViT，但仍把CNN先验置于最前；patch投影被描述为压缩，忽略32→32可满秩。", "scores": { "code_fidelity": "partly_supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "B first compresses each 4×4 patch", "judgment": "overstated", "assessment": "2通道×4×4=32输入、32输出的学习式投影不在维数上强迫压缩或丢失边界细节。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", 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"mean_test_ce": 0.8910881713964045 }, "p6_none": { "node_id": "candidate_cebf542b0c9dd216cf59", "mean_train_ce": 0.46459229208994657, "mean_test_ce": 0.697465454461053 }, "p12_none": { "node_id": "candidate_8311a8ee675aef20c38e", "mean_train_ce": 0.1095713679023902, "mean_test_ce": 0.41140049835667014 }, "p3_learned": { "node_id": "candidate_6c37ff6172a94ef6e3af", "mean_train_ce": 0.043305365988999256, "mean_test_ce": 0.11676968796382425 }, "p6_learned": { "node_id": "candidate_095f6edda493c046a96c", "mean_train_ce": 0.04081276130364131, "mean_test_ce": 0.14172115739347646 }, "p12_learned": { "node_id": "candidate_269a8e9e175414391617", "mean_train_ce": 0.05358990211288983, "mean_test_ce": 0.2854302610794548 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "已独立阅读本条完整公开正文，核对两条相邻关系及实际字母映射；覆盖完整不等于预测正确。", "answer_consistency_note": "本条正文给出的相对次序与提交排序一致；与其他重排回答之间的变化不混入本条一致性评分。" }, { "question_id": "q_7c4d6ba0c545202de44c8f79483454a8", "model": "gpt-6-luna", "ranking_correct": false, "summary_zh": "识别无PE与mean pooling限制，但仍以细粒度优先判断两条邻接关系；排序全反。保留其明确不确定性，不把错误排序等同于所有机制均错。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "partly_supported", "causal_calibration": "supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "Choice A’s 3×3 patches preserve the most local spatial detail and provide the largest token grid, giving its attention layers the best chance to extract location cues.", "judgment": "partly_supported", "assessment": "64-token及较细局部采样属实，但unordered token没有网格身份；本seed无PE控制CE为p3=0.9349、p6=0.7407、p12=0.3778，不能用细粒度直接推出更好位置推断。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "it therefore lacks a direct representation of global token order, which limits all three models’ ability to solve a relative-position task and makes the predicted gaps uncertain.", "judgment": "supported", "assessment": "对缺失直接token顺序的限定准确，且明确预期差异不确定，没有断言patch内位置也全部消失。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_7c4d6ba0c545202de44c8f79483454a8/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "bd3e577d9f6933ae0abcc5b0b0431d84a1965cf3bf5d22d812ddf3646734e447", "probe": null, "instance_id": "patch_coordinate_retention_s842902", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_21e3bc0b559aa842f774", "mean_train_ce": 0.8076861849986017, "mean_test_ce": 0.9349042777903378 }, "p6_none": { "node_id": "candidate_cffe94f10d775843b964", "mean_train_ce": 0.49123559065628797, "mean_test_ce": 0.74066974199377 }, "p12_none": { "node_id": "candidate_966cbcd0109e11fe7624", "mean_train_ce": 0.09820887906607823, "mean_test_ce": 0.3777853555511683 }, "p3_learned": { "node_id": "candidate_6e80eb53d23db89710cf", "mean_train_ce": 0.05790494961911463, "mean_test_ce": 0.12110790675797034 }, "p6_learned": { "node_id": "candidate_4dc751a92e6560f8ceca", "mean_train_ce": 0.055842820508587465, "mean_test_ce": 0.13072585669942782 }, "p12_learned": { "node_id": "candidate_40656e7f962a4cdaebf1", "mean_train_ce": 0.05304388912145441, "mean_test_ce": 0.22854750516125932 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "已独立阅读本条完整公开正文，核对两条相邻关系及实际字母映射；覆盖完整不等于预测正确。", "answer_consistency_note": "本条正文给出的相对次序与提交排序一致；与其他重排回答之间的变化不混入本条一致性评分。" }, { "question_id": "q_c179b9eb52ffd8f3f234de5863020a7a", "model": "gpt-6-luna", "ranking_correct": true, "summary_zh": "正确排序并指出无PE集合对称性及patch内联合几何；把同patch共现当主要优势仍是未单独测量的机制假设。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "partly_supported", "causal_calibration": "supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "making its representation permutation-invariant over patches.", "judgment": "supported", "assessment": "无PE、共享token模块加mean读出确实对token置换不变。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_c179b9eb52ffd8f3f234de5863020a7a/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "Choice A's 12×12 patches most often provide a joint receptive field for the two objects, and its 32-dimensional projection should retain enough coarse geometry", "judgment": "partly_supported", "assessment": "较大patch可同时覆盖更多对象几何，与结果方向相容；未独立测量两对象共现率或投影保真，不能认定其为唯一主因。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "if that information bottleneck dominates, C could overtake A", "judgment": "supported", "assessment": "显式保留压缩瓶颈这一竞争解释，适当地把排序写成有限预算预测。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "d944315105da922b74d376f3ba0ed28a4da3cc9f26255235755387eab11bb943", "probe": { "type": "letter_permutation", "source_question_id": "q_7c4d6ba0c545202de44c8f79483454a8" }, "instance_id": "patch_coordinate_retention_s842902", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_21e3bc0b559aa842f774", "mean_train_ce": 0.8076861849986017, "mean_test_ce": 0.9349042777903378 }, "p6_none": { "node_id": "candidate_cffe94f10d775843b964", "mean_train_ce": 0.49123559065628797, "mean_test_ce": 0.74066974199377 }, "p12_none": { "node_id": "candidate_966cbcd0109e11fe7624", "mean_train_ce": 0.09820887906607823, "mean_test_ce": 0.3777853555511683 }, "p3_learned": { "node_id": "candidate_6e80eb53d23db89710cf", "mean_train_ce": 0.05790494961911463, "mean_test_ce": 0.12110790675797034 }, "p6_learned": { "node_id": "candidate_4dc751a92e6560f8ceca", "mean_train_ce": 0.055842820508587465, "mean_test_ce": 0.13072585669942782 }, "p12_learned": { "node_id": "candidate_40656e7f962a4cdaebf1", "mean_train_ce": 0.05304388912145441, "mean_test_ce": 0.22854750516125932 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "已独立阅读本条完整公开正文，核对两条相邻关系及实际字母映射；覆盖完整不等于预测正确。", "answer_consistency_note": "本条正文给出的相对次序与提交排序一致；与其他重排回答之间的变化不混入本条一致性评分。" }, { "question_id": "q_76cac3671eecb9ff4306d1b86e28b45c", "model": "gpt-6-luna", "ranking_correct": true, "summary_zh": "正确排序，明确区分全局token顺序不可用与patch内间接位置线索，近随机仅作相对距离判断而非绝对损失定量。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "supported", "causal_calibration": "supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "Patch size nevertheless controls how much positional information is indirectly retained inside each patch.", "judgment": "supported", "assessment": "符合有序卷积核和无PE集合读出的结构区别；patch大小还联动参数和tokens，不能视为唯一隔离机制。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_76cac3671eecb9ff4306d1b86e28b45c/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "object centers occupy distinctive within-patch regions because the rendered centers lie roughly in the central image area", "judgment": "supported", "assessment": "实际中心proposal[5,18]并受拒绝采样约束，有限支持可使p12局部坐标提供象限先验。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/data/datasets/image_classification/ex_fe257d73a1dd6f116582f79082e71410/synthesize.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "it should therefore be closest to chance and worse than C.", "judgment": "supported", "assessment": "此处说p3在三者中最接近随机而非CE必为ln4；该相对预期与本实例0.8591/0.6362/0.3334一致。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "b6534656c9f5c853ef595c94946978da2952dc06101dbb7d15bd4daf05b3c758", "probe": null, "instance_id": "patch_coordinate_retention_s842903", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_9413340d1808e35e38d2", "mean_train_ce": 0.8394042456056923, "mean_test_ce": 0.8590657152235508 }, "p6_none": { "node_id": "candidate_10057c486fc74e1cc8c3", "mean_train_ce": 0.47532182844588533, "mean_test_ce": 0.6362490369938314 }, "p12_none": { "node_id": "candidate_9b70caa50778b52c8b2b", "mean_train_ce": 0.1284000096784439, "mean_test_ce": 0.33341567730531096 }, "p3_learned": { "node_id": "candidate_8ec7408c41affba5ba69", "mean_train_ce": 0.06069502418904449, "mean_test_ce": 0.11900378423524671 }, "p6_learned": { "node_id": "candidate_b0d33637a9a893bd3584", "mean_train_ce": 0.05756715495363096, "mean_test_ce": 0.14460775897896383 }, "p12_learned": { "node_id": "candidate_50d950c767425f971cc0", "mean_train_ce": 0.042319347230204585, "mean_test_ce": 0.2033218435390154 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "已独立阅读本条完整公开正文，核对两条相邻关系及实际字母映射；覆盖完整不等于预测正确。", "answer_consistency_note": "本条正文给出的相对次序与提交排序一致；与其他重排回答之间的变化不混入本条一致性评分。" }, { "question_id": "q_21ca0ff91a02c38e27e1bebe5867c24d", "model": "gpt-6-luna", "ranking_correct": true, "summary_zh": "正确区分跨patch顺序缺失与patch内位置保留，解释两条邻接关系并保留共现与优化不确定性。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "supported", "causal_calibration": "supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "They therefore have little direct access to the objects’ arrangement across patches.", "judgment": "supported", "assessment": "限定为缺少直接跨patch排列，不把所有位置线索抹除，与模型代码一致。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_21ca0ff91a02c38e27e1bebe5867c24d/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "Larger patches can preserve more within-patch spatial relationships", "judgment": "supported", "assessment": "较大局部坐标框架是与已观察方向及PE对照相容的非唯一解释；回答使用can而非保证。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "useful within-patch co-occurrence may be uncommon, and seed variation or optimization could narrow or reverse the differences.", "judgment": "supported", "assessment": "主动说明共现与训练优化未确定，未把实验总效应提升为表达能力定理。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "ad0e0c6c6a82b14ee3dc05c718b96c7e7a476d1cf162377273b6cb744016df42", "probe": { "type": "letter_permutation", "source_question_id": "q_76cac3671eecb9ff4306d1b86e28b45c" }, "instance_id": "patch_coordinate_retention_s842903", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_9413340d1808e35e38d2", 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"data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" } ], "response_sha256": "3154314663dcad540c282d14088605ebc77bb614cab89f3a1c5f16c51dd5f4a3", "probe": { "type": "letter_permutation", "source_question_id": "q_a03647bbfbcd65d2146723676eb79259" }, "instance_id": "operator_shape_generalization_s842903", "template_id": "operator_shape_generalization", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "cnn_spatial_lr003": { "node_id": "candidate_bc967178393fba3243e2", "mean_train_ce": 0.011284354424745402, "mean_test_ce": 0.04036032189725347 }, "vit_lr001": { "node_id": "candidate_3c047d3664cd4b3c1c5e", "mean_train_ce": 0.0801098879383062, "mean_test_ce": 1.1681748169939965 }, "mlp_lr001": { "node_id": "candidate_24443b0701b87898c365", "mean_train_ce": 0.2628516379336361, "mean_test_ce": 3.881161006167531 }, "cnn_spatial_lr001": { "node_id": "candidate_289d0a0358ab09a06844", "mean_train_ce": 0.03392823420881541, "mean_test_ce": 0.0599457420962608 }, "cnn_mean_lr001": { "node_id": "candidate_f42e6dcd3bdb79dafb0e", "mean_train_ce": 0.35121524626447354, "mean_test_ce": 0.38627267666743137 }, "cnn_mean_lr003": { "node_id": "candidate_384affc445971835418c", "mean_train_ce": 0.5175005892937747, "mean_test_ce": 0.5408244446484787 }, "vit_lr003": { "node_id": "candidate_49335a90bc48e247ec66", "mean_train_ce": 0.1136391817490221, "mean_test_ce": 0.6620928883203305 }, "mlp_lr003": { "node_id": "candidate_983fbd77986fead68406", "mean_train_ce": 0.08064754114093375, "mean_test_ce": 4.292790170758963 } } }, { "question_id": "q_5aa7514c54a69ed6cf080475ebf4304e", "model": "gpt-6.1-sol", "ranking_correct": false, "summary_zh": "承认无PE仍有局部几何，但以中等patch折中预测A>C>B，错把p12列最差；主要解释两组与A的比较，C>B的邻接理由较弱。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "partly_supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "local object location within each patch and interactions among patch contents still provide signal.", "judgment": "supported", "assessment": "准确保留patch内像素序及内容交互，不把无PE误写成完全无位置线索。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_5aa7514c54a69ed6cf080475ebf4304e/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "12×12 patches yield only four very coarse tokens and frequently merge or poorly resolve the small objects, losing directional and boundary information.", "judgment": "partly_supported", "assessment": "投影确实将大patch压到32维，但频繁丢失方向/边界未经单独测量；该实例p12 CE0.4114明显优于p6的0.6975与p3的0.8911。错误总排序不能单独否定压缩可能性，但此主导原因缺支持。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "A is preferred to C because 3×3 patches produce 64 tokens, diluting object-level evidence through mean pooling and making the fixed 300-update budget less favorable", "judgment": "partly_supported", "assessment": "p6优于p3这一邻接方向正确；mean稀释与优化成本未被单独隔离。正文重点比较A对C和A对B，没有充分展开提交顺序中C对B的具体差异。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "d15b89200c39310d58a42643162d155472105af9b8150223bb47e186b2fc2b6f", "probe": null, "instance_id": "patch_coordinate_retention_s842901", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_1c18fc797930667b5554", "mean_train_ce": 0.7912746695801616, "mean_test_ce": 0.8910881713964045 }, "p6_none": { "node_id": "candidate_cebf542b0c9dd216cf59", "mean_train_ce": 0.46459229208994657, "mean_test_ce": 0.697465454461053 }, "p12_none": { "node_id": "candidate_8311a8ee675aef20c38e", "mean_train_ce": 0.1095713679023902, "mean_test_ce": 0.41140049835667014 }, "p3_learned": { "node_id": "candidate_6c37ff6172a94ef6e3af", "mean_train_ce": 0.043305365988999256, "mean_test_ce": 0.11676968796382425 }, "p6_learned": { "node_id": "candidate_095f6edda493c046a96c", "mean_train_ce": 0.04081276130364131, "mean_test_ce": 0.14172115739347646 }, "p12_learned": { "node_id": "candidate_269a8e9e175414391617", "mean_train_ce": 0.05358990211288983, "mean_test_ce": 0.2854302610794548 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "逐条核对正文比较及字母映射，评分不由排序正误直接推出。", "answer_consistency_note": "正文与本条最终提交排序一致。" }, { "question_id": "q_8da922eda3a7c018f59492bcb3c32238", "model": "gpt-6.1-sol", "ranking_correct": true, "summary_zh": "正确识别token置换对称性、patch内坐标和边界片段；解释覆盖两条邻接，且不把缺PE误作必然随机。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "supported", "causal_calibration": "supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "the models must infer it from within-patch coordinates, object fragments, and channel co-occurrence.", "judgment": "supported", "assessment": "列出输入内容仍包含的三类潜在线索，避免把无PE推成无位置信息。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_8da922eda3a7c018f59492bcb3c32238/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "Given the restricted object-center range, within-patch coordinates and boundary crossings provide useful spatial cues.", "judgment": "supported", "assessment": "中心位于[5,18]并受方向平衡拒绝采样，有限支持与边界内容可提供位置先验；是否由具体神经元利用仍未单独测量。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/data/datasets/image_classification/ex_8ac3e0b6470c58835be190159e118cbd/synthesize.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "learning those cues may vary across seeds.", "judgment": "supported", "assessment": "对有限预算与种子变化有明确限定；没有宣称局部控制证明唯一因果机制。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "9c02b483847f5d37c3fd432dcc41a890af7921f5356bdb0552ae3f7ba98efc2c", "probe": { "type": "letter_permutation", "source_question_id": "q_5aa7514c54a69ed6cf080475ebf4304e" }, "instance_id": "patch_coordinate_retention_s842901", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_1c18fc797930667b5554", "mean_train_ce": 0.7912746695801616, "mean_test_ce": 0.8910881713964045 }, "p6_none": { "node_id": "candidate_cebf542b0c9dd216cf59", "mean_train_ce": 0.46459229208994657, "mean_test_ce": 0.697465454461053 }, "p12_none": { "node_id": "candidate_8311a8ee675aef20c38e", "mean_train_ce": 0.1095713679023902, "mean_test_ce": 0.41140049835667014 }, "p3_learned": { "node_id": "candidate_6c37ff6172a94ef6e3af", "mean_train_ce": 0.043305365988999256, "mean_test_ce": 0.11676968796382425 }, "p6_learned": { "node_id": "candidate_095f6edda493c046a96c", "mean_train_ce": 0.04081276130364131, "mean_test_ce": 0.14172115739347646 }, "p12_learned": { "node_id": "candidate_269a8e9e175414391617", "mean_train_ce": 0.05358990211288983, "mean_test_ce": 0.2854302610794548 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "已独立阅读本条完整公开正文，核对两条相邻关系及实际字母映射；覆盖完整不等于预测正确。", "answer_consistency_note": "本条正文给出的相对次序与提交排序一致；与其他重排回答之间的变化不混入本条一致性评分。" }, { "question_id": "q_76cac3671eecb9ff4306d1b86e28b45c", "model": "gpt-6.1-sol", "ranking_correct": true, "summary_zh": "排序正确且指出集合对称性，但把主要可学性归于两物体同patch共现，并提出weak ceiling，尚缺单独共现与表达上界验证。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "its mean-pooled token representation is permutation-invariant over patches.", "judgment": "supported", "assessment": "正确描述最终集合读出的对称性；不等于丢失patch内部有序像素。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_76cac3671eecb9ff4306d1b86e28b45c/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "the model can mainly use cases where both objects and their relative geometry are visible within a single patch.", "judgment": "partly_supported", "assessment": "同patch相对几何是合理线索，但跨patch碎片与[5,18]支持先验仍可用；未测量共现条件下贡献，mainly强于现有控制可识别范围。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "leaving the permutation-invariant model near a weak ceiling", "judgment": "partly_supported", "assessment": "p3终点较差与实测一致，但300步端点和集合对称性没有识别表达能力上界；本条weak ceiling含义含混，不能据此定为明确不可能性定理。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "The B-versus-C margin is materially uncertain because patch embedding capacity and the exact distribution of object placements could offset the co-location advantage", "judgment": "supported", "assessment": "明确保留容量与数据位置分布的竞争作用，有合理不确定性，故不将整条因果校准判incorrect。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "7ceef29d82a146641af228772ec1f508b1b66c571b4f957406637467c38f8af6", "probe": null, "instance_id": "patch_coordinate_retention_s842903", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_9413340d1808e35e38d2", "mean_train_ce": 0.8394042456056923, "mean_test_ce": 0.8590657152235508 }, "p6_none": { "node_id": "candidate_10057c486fc74e1cc8c3", "mean_train_ce": 0.47532182844588533, "mean_test_ce": 0.6362490369938314 }, "p12_none": { "node_id": "candidate_9b70caa50778b52c8b2b", "mean_train_ce": 0.1284000096784439, "mean_test_ce": 0.33341567730531096 }, "p3_learned": { "node_id": "candidate_8ec7408c41affba5ba69", "mean_train_ce": 0.06069502418904449, "mean_test_ce": 0.11900378423524671 }, "p6_learned": { "node_id": "candidate_b0d33637a9a893bd3584", "mean_train_ce": 0.05756715495363096, "mean_test_ce": 0.14460775897896383 }, "p12_learned": { "node_id": "candidate_50d950c767425f971cc0", "mean_train_ce": 0.042319347230204585, "mean_test_ce": 0.2033218435390154 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "逐条核对正文比较及字母映射，评分不由排序正误直接推出。", "answer_consistency_note": "正文与本条最终提交排序一致。" }, { "question_id": "q_21ca0ff91a02c38e27e1bebe5867c24d", "model": "gpt-6.1-sol", "ranking_correct": true, "summary_zh": "正确排列并保留patch内坐标、碎片及输入支持先验，对粗patch优势给出有条件机制。", "scores": { "code_fidelity": "supported", "mechanism_alignment": "supported", "causal_calibration": "supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "each patch projection still preserves information about positions *within* a patch.", "judgment": "supported", "assessment": "卷积核有有序像素权重；无PE不会删除这些输入自由度，不能保证任一已训练投影全保留但表述仅为保留信息。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_21ca0ff91a02c38e27e1bebe5867c24d/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "within-patch locations and boundary truncations also provide useful implicit spatial cues.", "judgment": "supported", "assessment": "[5,18]中心支持与对象跨patch边界提供可能的间接位置线索，与六格控制结果相容。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/data/datasets/image_classification/ex_fe257d73a1dd6f116582f79082e71410/synthesize.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "There is material uncertainty about how efficiently C learns its implicit spatial cues and how much A exploits overlap geometry", "judgment": "supported", "assessment": "明确区分可用输入线索与模型实际学习效率，因果强度合适。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "e442b11ce9c1c7d428d59f8b3e94e0d5c9587e3a975bdd02eb71bcc5b101241f", "probe": { "type": "letter_permutation", "source_question_id": "q_76cac3671eecb9ff4306d1b86e28b45c" }, "instance_id": "patch_coordinate_retention_s842903", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_9413340d1808e35e38d2", "mean_train_ce": 0.8394042456056923, "mean_test_ce": 0.8590657152235508 }, "p6_none": { "node_id": "candidate_10057c486fc74e1cc8c3", "mean_train_ce": 0.47532182844588533, "mean_test_ce": 0.6362490369938314 }, "p12_none": { "node_id": "candidate_9b70caa50778b52c8b2b", "mean_train_ce": 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"comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "the only route to location is the learned position embeddings", "judgment": "incorrect", "assessment": "patch内部像素顺序仍可编码局部坐标，mean只抹去token索引，不使全部位置线索只来自PE。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/explanation_supporting_checks.json" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" }, { "verbatim_public_quote": "can move only about 0.075 per coordinate in 75 Adam steps", "judgment": "incorrect", "assessment": "AdamW没有LR×steps硬位移界；同LR/steps/betas/eps/wd、全部梯度.075。此反例不是候选实际位移测量。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/explanation_supporting_checks.json" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" } ], "response_sha256": "8ab5aa1f77b14c5acd5ec98c9e58c6940bc4d83870615080928fc162b8949869", "probe": null, "instance_id": "operator_learning_speed_s842901", "template_id": "operator_learning_speed", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "mlp_75": { "node_id": "candidate_6dc2bfb1abd3acedb8ae", "mean_train_ce": 0.017555464248289354, "mean_test_ce": 0.027078771356173092 }, "cnn_75": { "node_id": "candidate_1ff88bee20b5750886ae", "mean_train_ce": 0.2197208803263493, "mean_test_ce": 0.22694993624463677 }, "vit_75": { "node_id": "candidate_984471b5ab216a29adf4", "mean_train_ce": 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"supported", "assessment": "近似中心矩读取是可行路线，随机强度意味着不是未经归一的精确中心。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" }, { "verbatim_public_quote": "Pooling destroys location unless the learned position embeddings", "judgment": "overstated", "assessment": "无PE时patch内顺序/数据支持也可携带位置关系；supp独立noPE控制显著超过chance，不能作必要性断言。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" } ], "response_sha256": "c04e1cef60fcf7fc32750a8f09b88f032539e6eba907eaa613afbdf52d50af79", "probe": { "type": "letter_permutation", "source_question_id": 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"data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/explanation_supporting_checks.json" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" }, { "verbatim_public_quote": "position embeddings that are still near their initial values", "judgment": "plausible_unmeasured", "assessment": "初始化尺度已知，但没有测候选最终position参数位移；75步本身不证明仍在初值附近，patch内部位置线索也不能忽略。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/explanation_supporting_checks.json" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" } ], "response_sha256": "aeaaf86fec942edada278037eca441e62a0fe9e9818f282c318bd957410d8fd1", "probe": null, "instance_id": "operator_learning_speed_s842902", "template_id": "operator_learning_speed", "public_fields_reviewed": [ "content", "explanation" ], 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"data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" }, { "verbatim_public_quote": "4×4 patches also lose fine location within each patch", "judgment": "incorrect", "assessment": "2×4×4到32的学习式投影可满秩，代码不强制丢弃内部细位置。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", 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"model": "claude-opus-5-5", "ranking_correct": true, "summary_zh": "位置范围、暴露预算与LR读取基本正确，排序正确；逐位置独立学习被夸成MLP必要条件。", "scores": { "code_fidelity": "partly_supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "must learn each shape at every position", "judgment": "overstated", "assessment": "MLP可以学习复用统计规律，未硬编码共享不意味着必须每位置独立记忆。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" }, { "verbatim_public_quote": "seen about 37 times each", "judgment": "overstated", "assessment": "1200×32/1024=37.5是平均样本暴露，采样有放回；每张图实际次数不同。该小误差不影响总体预算比较。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" } ], "response_sha256": "03c15214677afff795bfda4fad1730c69c10cd38a2384c407228cdaec8be85f8", "probe": { "type": "letter_permutation", "source_question_id": "q_dc6a1198c1d2e494edd7b3618c36bf0b" }, "instance_id": "operator_shape_generalization_s842902", "template_id": "operator_shape_generalization", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "cnn_spatial_lr003": { "node_id": "candidate_5eba7433689d84096466", "mean_train_ce": 0.01444872561237176, "mean_test_ce": 0.025722874704257492 }, "vit_lr001": { "node_id": "candidate_e73b093ae878fb579eb8", "mean_train_ce": 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"summary_zh": "识别CNN/ViT/MLP的共享结构差别，但将有限样本不足与逐位置记忆写成无条件结论。", "scores": { "code_fidelity": "partly_supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "must learn shape templates separately for every position", "judgment": "overstated", "assessment": "没有共享约束不证明MLP只能分别学模板；实测train/test差距支持当前泛化问题。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" }, { "verbatim_public_quote": "1024 samples cannot cover that", "judgment": "overstated", "assessment": "没有给出覆盖或样本复杂度下界；匹配LR控制也显示优化与泛化共同变化，不能将数量直接当作不可能性证明。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" } ], "response_sha256": "3473c938206a8cff0ba6464d0b0f68135b57782485e8dcaa7c136aed934f543c", "probe": null, "instance_id": "operator_shape_generalization_s842903", "template_id": "operator_shape_generalization", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "cnn_spatial_lr003": { "node_id": "candidate_bc967178393fba3243e2", "mean_train_ce": 0.011284354424745402, "mean_test_ce": 0.04036032189725347 }, "vit_lr001": { "node_id": "candidate_3c047d3664cd4b3c1c5e", "mean_train_ce": 0.0801098879383062, "mean_test_ce": 1.1681748169939965 }, "mlp_lr001": { "node_id": "candidate_24443b0701b87898c365", "mean_train_ce": 0.2628516379336361, "mean_test_ce": 3.881161006167531 }, "cnn_spatial_lr001": { "node_id": 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"supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "near-zero training loss but confident mistakes on held-out positions", "judgment": "incorrect", "assessment": "主MLP完整train CE均值约.263，test约3.881；泛化差距明确，但近零训练loss及错误按位置分布均未被证明。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" }, { "verbatim_public_quote": "may leave it less converged", "judgment": "plausible_unmeasured", "assessment": "ViT train≈.080而test≈1.168；提高LR至.003后train≈.114而test≈.662，描述性对照提示不能只以未收敛解释。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/models/image_classifier.py", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/src/architecture_iq/families/image_classification.py" ], "scope_limit": "当前固定合成数据与有限训练配方。主问题统计与描述性局部诊断分开；观察支持机制不意味着唯一原因，正确排序不自动证明解释。" } ], "response_sha256": "7b0990033a0b1e3234fc730145e1965acb797966a51c28444ff6cc76b7620845", "probe": { "type": "letter_permutation", "source_question_id": "q_a03647bbfbcd65d2146723676eb79259" }, "instance_id": "operator_shape_generalization_s842903", "template_id": "operator_shape_generalization", "public_fields_reviewed": [ "content", "explanation" ], "actual_comparison_evidence": { "cnn_spatial_lr003": { "node_id": "candidate_bc967178393fba3243e2", "mean_train_ce": 0.011284354424745402, "mean_test_ce": 0.04036032189725347 }, "vit_lr001": { "node_id": "candidate_3c047d3664cd4b3c1c5e", "mean_train_ce": 0.0801098879383062, "mean_test_ce": 1.1681748169939965 }, "mlp_lr001": { "node_id": "candidate_24443b0701b87898c365", "mean_train_ce": 0.2628516379336361, "mean_test_ce": 3.881161006167531 }, "cnn_spatial_lr001": { "node_id": "candidate_289d0a0358ab09a06844", "mean_train_ce": 0.03392823420881541, "mean_test_ce": 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"verbatim_public_quote": "That makes the within-patch position largely reveal which patch the object is in", "judgment": "partly_supported", "assessment": "[5,18]支持确可使p12局部坐标约束象限，但局部坐标重叠、投影压缩与对象片段仍带来歧义；这不是位置完整恢复证明。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/data/datasets/image_classification/ex_8ac3e0b6470c58835be190159e118cbd/synthesize.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "Its test cross-entropy should stay near ln 4 ≈ 1.386, possibly worse if it overfits noise.", "judgment": "incorrect", "assessment": "p3该seed确认test CE=0.8911、完整train CE=0.7913，明显低于ln4；无PE的排列对称性不推出随机水平。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "The only remaining signal is near-shape statistics, which are uninformative because the label is purely positional.", "judgment": "incorrect", "assessment": "这条排他性结构断言忽略patch内像素序、碎片关系和有限中心支持先验；因果校准I依据该无信息断言，而非单独依据CE数值预测错。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_5aa7514c54a69ed6cf080475ebf4304e/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "56f9742e22af94aa6f1e17435a6df3dbf77ca4cb24a4dabb5457d8203c96c001", "probe": null, "instance_id": "patch_coordinate_retention_s842901", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_1c18fc797930667b5554", "mean_train_ce": 0.7912746695801616, "mean_test_ce": 0.8910881713964045 }, "p6_none": { "node_id": "candidate_cebf542b0c9dd216cf59", "mean_train_ce": 0.46459229208994657, "mean_test_ce": 0.697465454461053 }, "p12_none": { "node_id": "candidate_8311a8ee675aef20c38e", "mean_train_ce": 0.1095713679023902, "mean_test_ce": 0.41140049835667014 }, "p3_learned": { "node_id": "candidate_6c37ff6172a94ef6e3af", "mean_train_ce": 0.043305365988999256, "mean_test_ce": 0.11676968796382425 }, "p6_learned": { "node_id": "candidate_095f6edda493c046a96c", "mean_train_ce": 0.04081276130364131, "mean_test_ce": 0.14172115739347646 }, "p12_learned": { "node_id": "candidate_269a8e9e175414391617", "mean_train_ce": 0.05358990211288983, "mean_test_ce": 0.2854302610794548 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "已独立阅读本条完整公开正文，核对两条相邻关系及实际字母映射；覆盖完整不等于预测正确。", "answer_consistency_note": "本条正文给出的相对次序与提交排序一致；与其他重排回答之间的变化不混入本条一致性评分。", "calibration_note": "incorrect针对本条almost no information、essentially unrecoverable、only uninformative signal或nearly all geometry discarded等结构性无信息断言；错误的近ln4端点预测另记为结果预测失准，不单独决定本维评分。" }, { "question_id": "q_8da922eda3a7c018f59492bcb3c32238", "model": "claude-opus-5-5", "ranking_correct": true, "summary_zh": "正确排序及局部像素有序判断，但对p3近随机和弱信号的预测过强，忽略实际低于随机的可学线索。", "scores": { "code_fidelity": "partly_supported", "mechanism_alignment": "partly_supported", "causal_calibration": "incorrect", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "where the patch-embedding conv weights encode pixel location.", "judgment": "supported", "assessment": "patch核权重区分局部像素索引，这是无PE仍保留位置内容的真实代码事实。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_8da922eda3a7c018f59492bcb3c32238/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "so almost no relational information survives. Expect near-chance cross entropy (about ln 4 ≈ 1.386).", "judgment": "incorrect", "assessment": "本实例p3确认CE0.8911；p6为0.6975。不能由token置换不变断言集合碎片几乎没有关系信息。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "ee0e1e5b3f4aa50f46de72f32f3c6819a472200548ea8d15c6610718a2d32e34", "probe": { "type": "letter_permutation", "source_question_id": "q_5aa7514c54a69ed6cf080475ebf4304e" }, "instance_id": "patch_coordinate_retention_s842901", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_1c18fc797930667b5554", "mean_train_ce": 0.7912746695801616, "mean_test_ce": 0.8910881713964045 }, "p6_none": { "node_id": "candidate_cebf542b0c9dd216cf59", "mean_train_ce": 0.46459229208994657, "mean_test_ce": 0.697465454461053 }, "p12_none": { "node_id": "candidate_8311a8ee675aef20c38e", "mean_train_ce": 0.1095713679023902, "mean_test_ce": 0.41140049835667014 }, "p3_learned": { "node_id": "candidate_6c37ff6172a94ef6e3af", "mean_train_ce": 0.043305365988999256, "mean_test_ce": 0.11676968796382425 }, "p6_learned": { "node_id": "candidate_095f6edda493c046a96c", "mean_train_ce": 0.04081276130364131, "mean_test_ce": 0.14172115739347646 }, "p12_learned": { "node_id": "candidate_269a8e9e175414391617", "mean_train_ce": 0.05358990211288983, "mean_test_ce": 0.2854302610794548 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "已独立阅读本条完整公开正文，核对两条相邻关系及实际字母映射；覆盖完整不等于预测正确。", "answer_consistency_note": "本条正文给出的相对次序与提交排序一致；与其他重排回答之间的变化不混入本条一致性评分。", "calibration_note": "incorrect针对本条almost no information、essentially unrecoverable、only uninformative signal或nearly all geometry discarded等结构性无信息断言；错误的近ln4端点预测另记为结果预测失准，不单独决定本维评分。" }, { "question_id": "q_7c4d6ba0c545202de44c8f79483454a8", "model": "claude-opus-5-5", "ranking_correct": true, "summary_zh": "正确识别无PE与粗patch优势，但把p3相对位置称为基本不可恢复并预期随机CE，强于代码和确认数据允许的结论。", "scores": { "code_fidelity": "partly_supported", "mechanism_alignment": "partly_supported", "causal_calibration": "incorrect", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "the patch-embedding conv, which encodes where content sits *inside* a patch, i.e. coordinates modulo the patch size.", "judgment": "supported", "assessment": "局部有序卷积确能携带patch内位置；模patch歧义是相关机制，但不穷尽碎片和支持先验。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_7c4d6ba0c545202de44c8f79483454a8/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "absolute and relative placement is essentially unrecoverable. A should therefore sit near chance cross entropy (about ln 4 ≈ 1.386).", "judgment": "incorrect", "assessment": "本实例p3 CE0.9349，完整train CE0.8077；不能称基本不可恢复，也未靠控制证明近随机。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "0ccf84d6a456e5edc369e260594c76d78d039f84af23e1bf7ff0e292d9254edf", "probe": null, "instance_id": "patch_coordinate_retention_s842902", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_21e3bc0b559aa842f774", "mean_train_ce": 0.8076861849986017, "mean_test_ce": 0.9349042777903378 }, "p6_none": { "node_id": "candidate_cffe94f10d775843b964", "mean_train_ce": 0.49123559065628797, "mean_test_ce": 0.74066974199377 }, "p12_none": { "node_id": "candidate_966cbcd0109e11fe7624", "mean_train_ce": 0.09820887906607823, "mean_test_ce": 0.3777853555511683 }, "p3_learned": { "node_id": "candidate_6e80eb53d23db89710cf", "mean_train_ce": 0.05790494961911463, "mean_test_ce": 0.12110790675797034 }, "p6_learned": { "node_id": "candidate_4dc751a92e6560f8ceca", "mean_train_ce": 0.055842820508587465, "mean_test_ce": 0.13072585669942782 }, "p12_learned": { "node_id": "candidate_40656e7f962a4cdaebf1", "mean_train_ce": 0.05304388912145441, "mean_test_ce": 0.22854750516125932 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "已独立阅读本条完整公开正文，核对两条相邻关系及实际字母映射；覆盖完整不等于预测正确。", "answer_consistency_note": "本条正文给出的相对次序与提交排序一致；与其他重排回答之间的变化不混入本条一致性评分。", "calibration_note": "incorrect针对本条almost no information、essentially unrecoverable、only uninformative signal或nearly all geometry discarded等结构性无信息断言；错误的近ln4端点预测另记为结果预测失准，不单独决定本维评分。" }, { "question_id": "q_c179b9eb52ffd8f3f234de5863020a7a", "model": "claude-opus-5-5", "ranking_correct": true, "summary_zh": "正确排序并利用中心支持解释p12；同时误把p3整体集合当接近无布局，近随机绝对损失与实测冲突。", "scores": { "code_fidelity": "partly_supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "So the in-patch coordinate largely reveals which quadrant a token is, recovering near-absolute positions even without position embeddings.", "judgment": "partly_supported", "assessment": "中心支持可产生象限先验，但恢复完整坐标依赖局部坐标歧义、形状碎片和学习；没有独立恢复率证明near-absolute。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/data/datasets/image_classification/ex_11e772539a2b33c6b69b1cd6423f0f5b/synthesize.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "B should stay near chance (cross entropy ≈ ln 4 ≈ 1.386), perhaps slightly lower or higher.", "judgment": "incorrect", "assessment": "本实例B=p3，确认CE0.9349而非ln4附近；对p3的无信息推断过强。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "4a0b97d511849b5fd1d8fb73a9b7bab127ad21c0cd9bba89117d952f4a6b1d08", "probe": { "type": "letter_permutation", "source_question_id": "q_7c4d6ba0c545202de44c8f79483454a8" }, "instance_id": "patch_coordinate_retention_s842902", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_21e3bc0b559aa842f774", "mean_train_ce": 0.8076861849986017, "mean_test_ce": 0.9349042777903378 }, "p6_none": { "node_id": "candidate_cffe94f10d775843b964", "mean_train_ce": 0.49123559065628797, "mean_test_ce": 0.74066974199377 }, "p12_none": { "node_id": "candidate_966cbcd0109e11fe7624", "mean_train_ce": 0.09820887906607823, "mean_test_ce": 0.3777853555511683 }, "p3_learned": { "node_id": "candidate_6e80eb53d23db89710cf", "mean_train_ce": 0.05790494961911463, "mean_test_ce": 0.12110790675797034 }, "p6_learned": { "node_id": "candidate_4dc751a92e6560f8ceca", "mean_train_ce": 0.055842820508587465, "mean_test_ce": 0.13072585669942782 }, "p12_learned": { "node_id": "candidate_40656e7f962a4cdaebf1", "mean_train_ce": 0.05304388912145441, "mean_test_ce": 0.22854750516125932 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "已独立阅读本条完整公开正文，核对两条相邻关系及实际字母映射；覆盖完整不等于预测正确。", "answer_consistency_note": "本条正文给出的相对次序与提交排序一致；与其他重排回答之间的变化不混入本条一致性评分。", "calibration_note": "近ln4预测被实测反驳，但预测失败本身不等于因果校准错误。本条保留不确定性，仅因near-absolute位置恢复等未经单独验证的强度而给partly_supported。" }, { "question_id": "q_76cac3671eecb9ff4306d1b86e28b45c", "model": "claude-opus-5-5", "ranking_correct": true, "summary_zh": "排序正确，提到边界碎片是有效线索；但断言p3几乎不保留关系且应近ln4，与确认结果相反。", "scores": { "code_fidelity": "partly_supported", "mechanism_alignment": "partly_supported", "causal_calibration": "incorrect", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "Within-patch offsets, plus how an object splits across the boundary at 12, carry a lot of usable left/right and up/down evidence.", "judgment": "partly_supported", "assessment": "这些是可用的结构线索，支持粗patch优势的合理解释；具体证据量和已训练模型利用程度未单独识别。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/data/datasets/image_classification/ex_fe257d73a1dd6f116582f79082e71410/synthesize.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "Almost no relative-position information survives the orderless pooling. A should stay near chance, around ln 4 ≈ 1.386", "judgment": "incorrect", "assessment": "A=p3的确认CE0.8591、train CE0.8394，均明显低于ln4；unordered并非所有关系信息都消失。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "a005876627b35fe71d3ca2e5a81672555f82f6b78ca69af6b0b54fd60bf01fe0", "probe": null, "instance_id": "patch_coordinate_retention_s842903", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_9413340d1808e35e38d2", "mean_train_ce": 0.8394042456056923, "mean_test_ce": 0.8590657152235508 }, "p6_none": { "node_id": "candidate_10057c486fc74e1cc8c3", "mean_train_ce": 0.47532182844588533, "mean_test_ce": 0.6362490369938314 }, "p12_none": { "node_id": "candidate_9b70caa50778b52c8b2b", "mean_train_ce": 0.1284000096784439, "mean_test_ce": 0.33341567730531096 }, "p3_learned": { "node_id": "candidate_8ec7408c41affba5ba69", "mean_train_ce": 0.06069502418904449, "mean_test_ce": 0.11900378423524671 }, "p6_learned": { "node_id": "candidate_b0d33637a9a893bd3584", "mean_train_ce": 0.05756715495363096, "mean_test_ce": 0.14460775897896383 }, "p12_learned": { "node_id": "candidate_50d950c767425f971cc0", "mean_train_ce": 0.042319347230204585, "mean_test_ce": 0.2033218435390154 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "已独立阅读本条完整公开正文，核对两条相邻关系及实际字母映射；覆盖完整不等于预测正确。", "answer_consistency_note": "本条正文给出的相对次序与提交排序一致；与其他重排回答之间的变化不混入本条一致性评分。", "calibration_note": "incorrect针对本条almost no information、essentially unrecoverable、only uninformative signal或nearly all geometry discarded等结构性无信息断言；错误的近ln4端点预测另记为结果预测失准，不单独决定本维评分。" }, { "question_id": "q_21ca0ff91a02c38e27e1bebe5867c24d", "model": "claude-opus-5-5", "ranking_correct": true, "summary_zh": "正确排序和局部坐标区间分析包含可用机制，但近完整位置编码、p3近随机及小gap判断均缺乏对应证据。", "scores": { "code_fidelity": "partly_supported", "mechanism_alignment": "partly_supported", "causal_calibration": "incorrect", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "This gives a near-complete positional code", "judgment": "partly_supported", "assessment": "[5,18]支持确实提供p12象限先验，但局部坐标重叠和线性压缩仍存在，不能把先验当近完整位置恢复。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/confirmation_tree/data/datasets/image_classification/ex_fe257d73a1dd6f116582f79082e71410/synthesize.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "Pooling across tokens throws away nearly all relative geometry. Cross-entropy should stay near ln 4.", "judgment": "incorrect", "assessment": "B=p3确认CE0.8591，非随机CE；mean pooling仅消除token索引，不抹去整个多重集合的几何内容。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "a4f145d3e54fd27f7e70ce4bc028116520d1da93d3c10a5bdfe229ff611c9180", "probe": { "type": "letter_permutation", "source_question_id": "q_76cac3671eecb9ff4306d1b86e28b45c" }, "instance_id": "patch_coordinate_retention_s842903", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_9413340d1808e35e38d2", "mean_train_ce": 0.8394042456056923, "mean_test_ce": 0.8590657152235508 }, "p6_none": { "node_id": "candidate_10057c486fc74e1cc8c3", "mean_train_ce": 0.47532182844588533, "mean_test_ce": 0.6362490369938314 }, "p12_none": { "node_id": "candidate_9b70caa50778b52c8b2b", "mean_train_ce": 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"model": "claude-haiku-5-5", "ranking_correct": true, "summary_zh": "正确排序且识别较高LR和参数规模，但声称patch内部细节必须靠跨token attention恢复，遗漏投影与逐token FF路径。", "scores": { "code_fidelity": "partly_supported", "mechanism_alignment": "partly_supported", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "local shape detail within a patch must be recovered by attention across tokens", "judgment": "incorrect", "assessment": "32→32 patch投影可保留局部细节，token FF与head也能读取；跨token attention不是恢复patch内部信息的必要条件。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "additional_evidence_paths": [ "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/resolved_plan.json", "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/quality_analysis/classification_quality.json", 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"排序正确，但无位置信号与所有模型近随机的解释不成立；more capacity含义含混，未被控制单独支持，不能简单等同更多参数。", "scores": { "code_fidelity": "incorrect", "mechanism_alignment": "incorrect", "causal_calibration": "incorrect", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "attention is permutation-equivariant, and the head mean-pools over tokens.", "judgment": "supported", "assessment": "正确区分attention等变与读出不变；这并不使patch内像素序消失。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_8da922eda3a7c018f59492bcb3c32238/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "Every choice is likely to sit near chance, with test cross-entropy close to ln 4 ≈ 1.386.", "judgment": "incorrect", "assessment": "该数据实例p3/p6/p12确认CE为0.8911/0.6975/0.4114，绝对损失与近随机前提冲突。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "Sixty-four tokens with 18 input values each give a longer attention sequence and more capacity.", "judgment": "partly_supported", "assessment": "more capacity含义不明确，不能把它自动解释成参数更多。p3参数确实最少，但激活/token自由度可能不同；本对照未隔离容量或校准，故该机制未获控制支持，而非仅凭参数更少判事实错误。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "since no positional signal exists to exploit.", "judgment": "incorrect", "assessment": "这条结构性断言直接遗漏patch内部有序像素投影及输入支持线索。保留code/causal的I是基于这句话；不是基于含混的capacity含义，也不是仅因近随机CE预测错。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_8da922eda3a7c018f59492bcb3c32238/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "406168c2a71f7c98630bd12a2b3bc21c40a50569b8431d4fd77142eb96dce7be", "probe": { "type": "letter_permutation", "source_question_id": "q_5aa7514c54a69ed6cf080475ebf4304e" }, "instance_id": "patch_coordinate_retention_s842901", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_1c18fc797930667b5554", "mean_train_ce": 0.7912746695801616, "mean_test_ce": 0.8910881713964045 }, "p6_none": { "node_id": "candidate_cebf542b0c9dd216cf59", "mean_train_ce": 0.46459229208994657, "mean_test_ce": 0.697465454461053 }, "p12_none": { "node_id": "candidate_8311a8ee675aef20c38e", "mean_train_ce": 0.1095713679023902, "mean_test_ce": 0.41140049835667014 }, "p3_learned": { "node_id": "candidate_6c37ff6172a94ef6e3af", "mean_train_ce": 0.043305365988999256, "mean_test_ce": 0.11676968796382425 }, "p6_learned": { "node_id": "candidate_095f6edda493c046a96c", "mean_train_ce": 0.04081276130364131, "mean_test_ce": 0.14172115739347646 }, "p12_learned": { "node_id": "candidate_269a8e9e175414391617", "mean_train_ce": 0.05358990211288983, "mean_test_ce": 0.2854302610794548 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "已独立阅读本条完整公开正文，核对两条相邻关系及实际字母映射；覆盖完整不等于预测正确。", "answer_consistency_note": "本条正文给出的相对次序与提交排序一致；与其他重排回答之间的变化不混入本条一致性评分。", "calibration_note": "因果校准I针对明确声称no positional signal exists；近随机损失预测失准与capacity歧义分别记录。" }, { "question_id": "q_7c4d6ba0c545202de44c8f79483454a8", "model": "claude-haiku-5-5", "ranking_correct": false, "summary_zh": "排序全反，虽指出排列不变，却仍将无身份的token网格当定位优势；相同初始权重的说法也错误。", "scores": { "code_fidelity": "incorrect", "mechanism_alignment": "incorrect", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "the same initial weights per seed.", "judgment": "incorrect", "assessment": "跨patch投影形状与initial-state hash不同；配对的是批次随机流，不是同权重。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "Finer tokens give more chances for the content to carry partial location information (e.g., which token contains the object), so A has the most usable signal.", "judgment": "incorrect", "assessment": "模型没有token索引可读，哪一个token包含对象不能直接恢复网格位置；实测无PE主排序相反，PE控制的反转也否定无条件细粒度优势。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/control_audit.json", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "the mean-pooled output of the attention blocks is permutation-invariant over tokens.", "judgment": "supported", "assessment": "这条代码事实本身正确，应与错误的后续定位推断分别评分。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_7c4d6ba0c545202de44c8f79483454a8/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" } ], "response_sha256": "776042db829164326f28e09f816e097c11d9a335d115fc61ab25bf3dcf2e3670", "probe": null, "instance_id": "patch_coordinate_retention_s842902", "actual_comparison_evidence": { "p3_none": { "node_id": "candidate_21e3bc0b559aa842f774", "mean_train_ce": 0.8076861849986017, "mean_test_ce": 0.9349042777903378 }, "p6_none": { "node_id": "candidate_cffe94f10d775843b964", "mean_train_ce": 0.49123559065628797, "mean_test_ce": 0.74066974199377 }, "p12_none": { "node_id": "candidate_966cbcd0109e11fe7624", "mean_train_ce": 0.09820887906607823, "mean_test_ce": 0.3777853555511683 }, "p3_learned": { "node_id": "candidate_6e80eb53d23db89710cf", "mean_train_ce": 0.05790494961911463, "mean_test_ce": 0.12110790675797034 }, "p6_learned": { "node_id": "candidate_4dc751a92e6560f8ceca", "mean_train_ce": 0.055842820508587465, "mean_test_ce": 0.13072585669942782 }, "p12_learned": { "node_id": "candidate_40656e7f962a4cdaebf1", "mean_train_ce": 0.05304388912145441, "mean_test_ce": 0.22854750516125932 } }, "public_fields_reviewed": [ "content", "explanation" ], "comparison_coverage_note": "已独立阅读本条完整公开正文，核对两条相邻关系及实际字母映射；覆盖完整不等于预测正确。", "answer_consistency_note": "本条正文给出的相对次序与提交排序一致；与其他重排回答之间的变化不混入本条一致性评分。" }, { "question_id": "q_c179b9eb52ffd8f3f234de5863020a7a", "model": "claude-haiku-5-5", "ranking_correct": false, "summary_zh": "正确列出patch与token数，却以空间网格精度直接推断位置任务优势，漏掉集合对称性下粗patch保留的局部几何。", "scores": { "code_fidelity": "partly_supported", "mechanism_alignment": "incorrect", "causal_calibration": "partly_supported", "comparison_coverage": "supported", "answer_consistency": "supported" }, "annotations": [ { "verbatim_public_quote": "Because there is no positional embedding, the model must recover location from patch content alone.", "judgment": "supported", "assessment": "应依赖patch内容、局部像素序及支持先验，不能直接读token索引。", "evidence_path": "data/task_expansion/vision_batch/image_ranking_supplement_20261015/classification/questions/q_c179b9eb52ffd8f3f234de5863020a7a/prompt_code/A/model.py", "scope_limit": "仅限当前固定合成数据与300步训练。主排序为已确认总干预效应；learned-PE六格控制只作描述性机制证据，不新增显著性结论，也不唯一识别位置、容量或优化机制。" }, { "verbatim_public_quote": "Patch 3 retains the most spatial detail and the most attention structure, so it should reach the lowest cross entropy.", "judgment": "incorrect", "assessment": "无PE下更多tokens不提供全局网格坐标；确认p12