{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:G7PPI4VK5CLXCTG454CHOAT3X3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"fb65d6c22827edb69f9f9fc02a3970a1698dfd8a729b0ab73abc63a58d52d6b3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-12T23:15:47Z","title_canon_sha256":"2a6eff238dddf7c3b1b3cb6187fe01503e90a704b0dd362010a2622678e6b789"},"schema_version":"1.0","source":{"id":"2605.12816","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12816","created_at":"2026-05-18T03:09:12Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12816v1","created_at":"2026-05-18T03:09:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12816","created_at":"2026-05-18T03:09:12Z"},{"alias_kind":"pith_short_12","alias_value":"G7PPI4VK5CLX","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"G7PPI4VK5CLXCTG4","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"G7PPI4VK","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:c118379827a1c1678585c8959ddc11cdbcd7833f4c0d7b0379180a27628ecdf0","target":"graph","created_at":"2026-05-18T03:09:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"AGOP-Weighted achieves 44% higher mIoU than IG on linear tasks; AGOP-Global achieves 7x higher mIoU than IG on multiplicative tasks (where IG falls below random) at zero inference cost. Both findings generalise to ResNet-18 on CLEVR-XAI (+18% and +37% respectively)."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the AGOP matrix computed over the training distribution supplies an unbiased prior that reliably suppresses gradient noise for individual test samples without introducing systematic errors when the test distribution differs from training."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"AGOP-based attribution methods outperform Integrated Gradients and other baselines on pixel-level ground truth benchmarks for explaining image classifier decisions, with AGOP-Global offering zero inference cost."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"The Average Gradient Outer Product matrix from training data supplies a prior that improves per-sample attribution maps in image classifiers."}],"snapshot_sha256":"85b292360a804d1a222f56982a6a683bb414df42e4b6356f6096278c52eef7a3"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"f778f73844b0ec0e41619c1848290616fd2b1f49494945c0005c474fd75936d0"},"paper":{"abstract_excerpt":"The Average Gradient Outer Product (AGOP) governs feature learning in neural networks: the Neural Feature Ansatz states that weight Gram matrices at each layer align with the corresponding AGOP matrices computed over the training distribution. We ask a complementary question: can this same quantity serve as a post-hoc attribution method for explaining individual predictions? We introduce AGOP-Weighted: a novel attribution method that multiplies the per-sample gradient by sqrt(diag(M) / max diag(M)), a training-distribution prior that suppresses gradient noise and amplifies consistently importa","authors_text":"Raj Kiran Gupta Katakam","cross_cats":[],"headline":"The Average Gradient Outer Product matrix from training data supplies a prior that improves per-sample attribution maps in image classifiers.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-12T23:15:47Z","title":"AGOP as Explanation: From Feature Learning to Per-Sample Attribution in Image Classifiers"},"references":{"count":15,"internal_anchors":2,"resolved_work":15,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"M. Sundararajan, A. Taly, Q. Yan, Axiomatic attribution for deep networks, in: Proceedings of ICML 2017, 2017, pp. 3319–3328","work_id":"d23bb228-7c70-42a3-b123-17e97dcca8fa","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-CAM: Visual explanations from deep networks via gradient-based localization, in: Proceedings of ICCV 2017, 2017, pp. 618–62","work_id":"8474b59e-b5eb-43b8-b572-33ac93f63837","year":2017},{"cited_arxiv_id":"1706.03825","doi":"","is_internal_anchor":true,"ref_index":3,"title":"SmoothGrad: removing noise by adding noise","work_id":"9fe7734e-4726-441f-abc1-88bbce75815c","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"A. Radhakrishnan, D. Beaglehole, P. Pandit, M. Belkin, Mechanism for feature learning in neural networks and backpropagation-free machine learning models, Science 383 (2024) 1461–1467","work_id":"b190d5b9-bbf7-48ca-8c59-ebd2bf72aca0","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"D. Beaglehole, A. Radhakrishnan, P. Pandit, M. Belkin, Mechanism of feature learning in convolu- tional neural networks, arXiv preprint arXiv:2309.00570 (2024)","work_id":"f6bf64bf-821d-44ed-9deb-4902c406c10c","year":2024}],"snapshot_sha256":"4082dd7c7aa28d28c368814844f6c846766f0637b8380a92ea632fc27a623400"},"source":{"id":"2605.12816","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T20:02:47.287810Z","id":"3ecc3eaa-c0f4-4902-8b88-bdcf139b8a27","model_set":{"reader":"grok-4.3"},"one_line_summary":"AGOP-based attribution methods outperform Integrated Gradients and other baselines on pixel-level ground truth benchmarks for explaining image classifier decisions, with AGOP-Global offering zero inference cost.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"The Average Gradient Outer Product matrix from training data supplies a prior that improves per-sample attribution maps in image classifiers.","strongest_claim":"AGOP-Weighted achieves 44% higher mIoU than IG on linear tasks; AGOP-Global achieves 7x higher mIoU than IG on multiplicative tasks (where IG falls below random) at zero inference cost. Both findings generalise to ResNet-18 on CLEVR-XAI (+18% and +37% respectively).","weakest_assumption":"That the AGOP matrix computed over the training distribution supplies an unbiased prior that reliably suppresses gradient noise for individual test samples without introducing systematic errors when the test distribution differs from training."}},"verdict_id":"3ecc3eaa-c0f4-4902-8b88-bdcf139b8a27"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:86cbde42414c87a4c429be1d4f220207034f3d2fa892fce7544297d9ec66d297","target":"record","created_at":"2026-05-18T03:09:12Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"fb65d6c22827edb69f9f9fc02a3970a1698dfd8a729b0ab73abc63a58d52d6b3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-12T23:15:47Z","title_canon_sha256":"2a6eff238dddf7c3b1b3cb6187fe01503e90a704b0dd362010a2622678e6b789"},"schema_version":"1.0","source":{"id":"2605.12816","kind":"arxiv","version":1}},"canonical_sha256":"37def472aae897714cdcef0477027bbec019eac802dd4fb9ae4b763f9203ee2c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"37def472aae897714cdcef0477027bbec019eac802dd4fb9ae4b763f9203ee2c","first_computed_at":"2026-05-18T03:09:12.302212Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:09:12.302212Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4DlOOuHoPocdpaZt5vTrB7PslIfhu2IOduhvaUl5i4LXRyB2hryvnDibw2YIZGK5QHEnqvPE5hpeHyXJ7It5Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:09:12.302890Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12816","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:86cbde42414c87a4c429be1d4f220207034f3d2fa892fce7544297d9ec66d297","sha256:c118379827a1c1678585c8959ddc11cdbcd7833f4c0d7b0379180a27628ecdf0"],"state_sha256":"52a3ccebd6e120cb8418e981c1627a2da5bff25a557d8882c629e4867fb5406c"}