{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:N3IDXNM7TYBCPPQIQQMYEYQ4YI","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":"d80a48d91e9915a4aac8a3c43770de105b2da0a8dbe5d50aa05033269c82e476","cross_cats_sorted":["cs.LG","math.OC","math.PR","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-05-23T21:50:54Z","title_canon_sha256":"23e4b19de055bc9f89621367eeb257c781d803a522879661347bca5a5de20c93"},"schema_version":"1.0","source":{"id":"2405.15074","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.15074","created_at":"2026-07-05T10:51:03Z"},{"alias_kind":"arxiv_version","alias_value":"2405.15074v3","created_at":"2026-07-05T10:51:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.15074","created_at":"2026-07-05T10:51:03Z"},{"alias_kind":"pith_short_12","alias_value":"N3IDXNM7TYBC","created_at":"2026-07-05T10:51:03Z"},{"alias_kind":"pith_short_16","alias_value":"N3IDXNM7TYBCPPQI","created_at":"2026-07-05T10:51:03Z"},{"alias_kind":"pith_short_8","alias_value":"N3IDXNM7","created_at":"2026-07-05T10:51:03Z"}],"graph_snapshots":[{"event_id":"sha256:732bc73b41598a6155fdd6526e4c1869d43e18124680a3d1c9387b2137580ca6","target":"graph","created_at":"2026-07-05T10:51:03Z","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/2405.15074/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We consider the solvable neural scaling model with three parameters: data complexity, target complexity, and model-parameter-count. We use this neural scaling model to derive new predictions about the compute-limited, infinite-data scaling law regime. To train the neural scaling model, we run one-pass stochastic gradient descent on a mean-squared loss. We derive a representation of the loss curves which holds over all iteration counts and improves in accuracy as the model parameter count grows. We then analyze the compute-optimal model-parameter-count, and identify 4 phases (+3 subphases) in t","authors_text":"Courtney Paquette, Elliot Paquette, Jeffrey Pennington, Lechao Xiao","cross_cats":["cs.LG","math.OC","math.PR","math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-05-23T21:50:54Z","title":"4+3 Phases of Compute-Optimal Neural Scaling Laws"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.15074","kind":"arxiv","version":3},"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":"sha256:7b6c57dbed7f3e4d645ae5ca00e4dc9c6584fb085aa6f4a78fe2ddccb350d4b0","target":"record","created_at":"2026-07-05T10:51:03Z","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":"d80a48d91e9915a4aac8a3c43770de105b2da0a8dbe5d50aa05033269c82e476","cross_cats_sorted":["cs.LG","math.OC","math.PR","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2024-05-23T21:50:54Z","title_canon_sha256":"23e4b19de055bc9f89621367eeb257c781d803a522879661347bca5a5de20c93"},"schema_version":"1.0","source":{"id":"2405.15074","kind":"arxiv","version":3}},"canonical_sha256":"6ed03bb59f9e0227be08841982621cc20fae27b3cdc3a8debde207d90b816919","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6ed03bb59f9e0227be08841982621cc20fae27b3cdc3a8debde207d90b816919","first_computed_at":"2026-07-05T10:51:03.664345Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:51:03.664345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HrhTMF4igSJcN0qFHfShIudSGPfoHAgcIoJFOHErRfiaOcPyWsMqtwGQ5GOtYlZSQaev1loIYGW+vkU2rbC+Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:51:03.664848Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.15074","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7b6c57dbed7f3e4d645ae5ca00e4dc9c6584fb085aa6f4a78fe2ddccb350d4b0","sha256:732bc73b41598a6155fdd6526e4c1869d43e18124680a3d1c9387b2137580ca6"],"state_sha256":"f70809f90266d828222bfe12176d2f82274ccf6acf640df9a5568c8b4d53d82d"}