{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:J4J32G4EOAKJTM5LZXIHFUTSK6","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":"ad1fea995a22185529f3d9417149332d00e7f0dde8f45b279cbb24f3d52eaaa1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-25T15:48:41Z","title_canon_sha256":"2957c46ea0a5ae4110d34aa23e14481fd9d235a0e7bc09ea31f31472f2507216"},"schema_version":"1.0","source":{"id":"2504.18437","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.18437","created_at":"2026-07-05T10:54:07Z"},{"alias_kind":"arxiv_version","alias_value":"2504.18437v1","created_at":"2026-07-05T10:54:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.18437","created_at":"2026-07-05T10:54:07Z"},{"alias_kind":"pith_short_12","alias_value":"J4J32G4EOAKJ","created_at":"2026-07-05T10:54:07Z"},{"alias_kind":"pith_short_16","alias_value":"J4J32G4EOAKJTM5L","created_at":"2026-07-05T10:54:07Z"},{"alias_kind":"pith_short_8","alias_value":"J4J32G4E","created_at":"2026-07-05T10:54:07Z"}],"graph_snapshots":[{"event_id":"sha256:e3d572a68ef522eda0bf764f0a509f2aeb1ab788017c5400c8d81147262b8265","target":"graph","created_at":"2026-07-05T10:54:07Z","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/2504.18437/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Class-Incremental Learning (CIL) is a critical capability for real-world applications, enabling learning systems to adapt to new tasks while retaining knowledge from previous ones. Recent advancements in pre-trained models (PTMs) have significantly advanced the field of CIL, demonstrating superior performance over traditional methods. However, understanding how features evolve and are distributed across incremental tasks remains an open challenge. In this paper, we propose a novel approach to modeling feature evolution in PTM-based CIL through the lens of neural collapse (NC), a striking pheno","authors_text":"John E. Hopcroft, Kun He, Shuoxi Zhang, Zijian Song","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-25T15:48:41Z","title":"Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.18437","kind":"arxiv","version":1},"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":"sha256:3e6bb6b2cfa4125e3a077ea63b65615181bf25f80ab6f42ffc1f703c7a2307f2","target":"record","created_at":"2026-07-05T10:54:07Z","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":"ad1fea995a22185529f3d9417149332d00e7f0dde8f45b279cbb24f3d52eaaa1","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-25T15:48:41Z","title_canon_sha256":"2957c46ea0a5ae4110d34aa23e14481fd9d235a0e7bc09ea31f31472f2507216"},"schema_version":"1.0","source":{"id":"2504.18437","kind":"arxiv","version":1}},"canonical_sha256":"4f13bd1b84701499b3abcdd072d27257b6a1ea88b2fa8444e34ded06b47d479b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4f13bd1b84701499b3abcdd072d27257b6a1ea88b2fa8444e34ded06b47d479b","first_computed_at":"2026-07-05T10:54:07.212221Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:54:07.212221Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9CNqot4cVjYshDWy14ZMKa7wZLS+YPI2pLL9elstw+XmfsGt1/+HTTh1Wu+94HHVh0Ions/c5UlGcDVqtpIJCg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:54:07.212636Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.18437","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3e6bb6b2cfa4125e3a077ea63b65615181bf25f80ab6f42ffc1f703c7a2307f2","sha256:e3d572a68ef522eda0bf764f0a509f2aeb1ab788017c5400c8d81147262b8265"],"state_sha256":"fded5b41e7dd7b93eaf9773d201850d9ef3a7296a0c7b68f94fb8eaf491c5d2f"}