{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:IEBWWZMW662P2ZRKKAUM7M4P4Z","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":"e2e9732572379973385baf16aeaadd6e2af9ffc2587ddc2326c0137e85f80e3a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-18T10:23:06Z","title_canon_sha256":"e84cf96b6e41fe6718a80604036a66859e6cb47014f1ba759474190ad96ee5df"},"schema_version":"1.0","source":{"id":"2605.18180","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.18180","created_at":"2026-05-20T00:05:49Z"},{"alias_kind":"arxiv_version","alias_value":"2605.18180v1","created_at":"2026-05-20T00:05:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18180","created_at":"2026-05-20T00:05:49Z"},{"alias_kind":"pith_short_12","alias_value":"IEBWWZMW662P","created_at":"2026-05-20T00:05:49Z"},{"alias_kind":"pith_short_16","alias_value":"IEBWWZMW662P2ZRK","created_at":"2026-05-20T00:05:49Z"},{"alias_kind":"pith_short_8","alias_value":"IEBWWZMW","created_at":"2026-05-20T00:05:49Z"}],"graph_snapshots":[{"event_id":"sha256:96bf2c15a5c17346ade02b9a2a5e55baee26c6fdc7337882c423850807797c80","target":"graph","created_at":"2026-05-20T00:05:49Z","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":[{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T23:41:59.030110Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T23:33:35.343507Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.18180/integrity.json","findings":[],"snapshot_sha256":"70b0e9d211df93c6fce0ca2864d268d35e006b29c9224661168a935e1815225c","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Wide neural networks in the feature-learning regime drive modern deep learning, and yet they remain far less studied than their kernel-regime counterparts. We consider a critical yet under-explored difference between these two regimes: the regulariser and prior implied by gradient flow training. This canonical regularisation property is well-studied in kernel regime networks -- of all the infinite global minima, gradient flow selects exactly the vanishing ridge solution -- and underpins the celebrated NN-GP correspondence, precisely allowing the modelling of noise during training. However, we ","authors_text":"George Whittle, Juliusz Ziomek, Maike A. Osborne, Natalia Ares, Pranav Vaidhyanathan","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-18T10:23:06Z","title":"Canonical Regularisation of Wide Feature-Learning Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18180","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:b47705a29d95dd4e8bef065b6a1b3b344ec327740873f45bc73627b98e031bfa","target":"record","created_at":"2026-05-20T00:05:49Z","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":"e2e9732572379973385baf16aeaadd6e2af9ffc2587ddc2326c0137e85f80e3a","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-18T10:23:06Z","title_canon_sha256":"e84cf96b6e41fe6718a80604036a66859e6cb47014f1ba759474190ad96ee5df"},"schema_version":"1.0","source":{"id":"2605.18180","kind":"arxiv","version":1}},"canonical_sha256":"41036b6596f7b4fd662a5028cfb38fe657937efd998810a1b43073fa53fb326f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"41036b6596f7b4fd662a5028cfb38fe657937efd998810a1b43073fa53fb326f","first_computed_at":"2026-05-20T00:05:49.533170Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:05:49.533170Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HhrtdrwlJ/RAi8ILJ+gzetljiXgy9E2oZi8JV77jCkIDGcSX45dp1A0Q84h05fiVP35jP7yDmhq7xisMhi4gBg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:05:49.533788Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.18180","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b47705a29d95dd4e8bef065b6a1b3b344ec327740873f45bc73627b98e031bfa","sha256:96bf2c15a5c17346ade02b9a2a5e55baee26c6fdc7337882c423850807797c80"],"state_sha256":"a346533c7bfdad9b2e9effa4e0f0ff7f3bcb4f7e87df09dc603abf05565f6848"}