{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:VN5F56PDGTV2JOYYQXNNYR3PDE","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":"ce9eb6ff549cfef59aaad469ef4a16ed99a02a537d251f962f5f058882db79b1","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-24T11:10:29Z","title_canon_sha256":"c1fb3b9496e7d9a51228bfd68ec0831ea386b9ab6d042fda544d7ad76081b290"},"schema_version":"1.0","source":{"id":"2601.19947","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.19947","created_at":"2026-05-28T01:04:35Z"},{"alias_kind":"arxiv_version","alias_value":"2601.19947v2","created_at":"2026-05-28T01:04:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.19947","created_at":"2026-05-28T01:04:35Z"},{"alias_kind":"pith_short_12","alias_value":"VN5F56PDGTV2","created_at":"2026-05-28T01:04:35Z"},{"alias_kind":"pith_short_16","alias_value":"VN5F56PDGTV2JOYY","created_at":"2026-05-28T01:04:35Z"},{"alias_kind":"pith_short_8","alias_value":"VN5F56PD","created_at":"2026-05-28T01:04:35Z"}],"graph_snapshots":[{"event_id":"sha256:eab3dcf31a6fd1d35f8d6a24f9e12072525b08f3e1db426ca19baf702b9d0650","target":"graph","created_at":"2026-05-28T01:04:35Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2601.19947/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning from Noisy Labels (LNL) remains a fundamental challenge in deep learning because real-world datasets often contain corrupted annotations. Most existing methods rely on label correction or sample selection mechanisms. In contrast, we study LNL from an optimization perspective by establishing a theoretical connection between label noise and the flatness-seeking behavior of Sharpness-Aware Minimization (SAM). Based on this analysis, we propose Noise-Compensated Sharpness-Aware Minimization (NCSAM), which uses a noise-compensated perturbation to counteract the optimization bias induced by","authors_text":"Jiayu Xu, Junbiao Pang","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-24T11:10:29Z","title":"NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.19947","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:49cd630de7e0f6ef2455933006716faa14da0d794f26b1d463cdd77ae7db130c","target":"record","created_at":"2026-05-28T01:04:35Z","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":"ce9eb6ff549cfef59aaad469ef4a16ed99a02a537d251f962f5f058882db79b1","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-01-24T11:10:29Z","title_canon_sha256":"c1fb3b9496e7d9a51228bfd68ec0831ea386b9ab6d042fda544d7ad76081b290"},"schema_version":"1.0","source":{"id":"2601.19947","kind":"arxiv","version":2}},"canonical_sha256":"ab7a5ef9e334eba4bb1885dadc476f19347a284d3bf66b64c90b79d7ac43b682","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ab7a5ef9e334eba4bb1885dadc476f19347a284d3bf66b64c90b79d7ac43b682","first_computed_at":"2026-05-28T01:04:35.733338Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-28T01:04:35.733338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ay5WO6fFwIGSkeChncOwFstQXAQOoJ/iFx4nR0rDBfcBvBGdg+5iS/M5gaogKFlzpjCFxS8KonOJngecL8OICQ==","signature_status":"signed_v1","signed_at":"2026-05-28T01:04:35.733811Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.19947","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:49cd630de7e0f6ef2455933006716faa14da0d794f26b1d463cdd77ae7db130c","sha256:eab3dcf31a6fd1d35f8d6a24f9e12072525b08f3e1db426ca19baf702b9d0650"],"state_sha256":"a7883f73db96790b957f69895e2b9594f416a7b78d95a558b0114a958924a6b1"}