{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:P4PBVBYLH5CSVBYLLIPH233S5B","short_pith_number":"pith:P4PBVBYL","schema_version":"1.0","canonical_sha256":"7f1e1a870b3f452a870b5a1e7d6f72e858aa2a149863fbc01f260559e8adfe06","source":{"kind":"arxiv","id":"2310.18988","version":1},"attestation_state":"computed","paper":{"title":"A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alan Jeffares, Alicia Curth, Mihaela van der Schaar","submitted_at":"2023-10-29T12:05:39Z","abstract_excerpt":"Conventional statistical wisdom established a well-understood relationship between model complexity and prediction error, typically presented as a U-shaped curve reflecting a transition between under- and overfitting regimes. However, motivated by the success of overparametrized neural networks, recent influential work has suggested this theory to be generally incomplete, introducing an additional regime that exhibits a second descent in test error as the parameter count p grows past sample size n - a phenomenon dubbed double descent. While most attention has naturally been given to the deep-l"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2310.18988","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-10-29T12:05:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"8f83d4e4360b6e8f4c92f3538fe36ee8134f9e12401fb79dc3d46fdb80219c1f","abstract_canon_sha256":"904b44e129961861488a83704c2a6863368559ac1d331a38f94a28cf545ccfdd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:06:44.372153Z","signature_b64":"ObkjD6ixiAE/KZrTZ7K51daZ4e39gyJ5+YVRh+uUct7OpDX4HBURydwiqDoT1vrBpPnJ8YFmeA4oZP0wacg0BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f1e1a870b3f452a870b5a1e7d6f72e858aa2a149863fbc01f260559e8adfe06","last_reissued_at":"2026-07-05T07:06:44.371636Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:06:44.371636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alan Jeffares, Alicia Curth, Mihaela van der Schaar","submitted_at":"2023-10-29T12:05:39Z","abstract_excerpt":"Conventional statistical wisdom established a well-understood relationship between model complexity and prediction error, typically presented as a U-shaped curve reflecting a transition between under- and overfitting regimes. However, motivated by the success of overparametrized neural networks, recent influential work has suggested this theory to be generally incomplete, introducing an additional regime that exhibits a second descent in test error as the parameter count p grows past sample size n - a phenomenon dubbed double descent. While most attention has naturally been given to the deep-l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.18988","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.18988/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2310.18988","created_at":"2026-07-05T07:06:44.371705+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.18988v1","created_at":"2026-07-05T07:06:44.371705+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.18988","created_at":"2026-07-05T07:06:44.371705+00:00"},{"alias_kind":"pith_short_12","alias_value":"P4PBVBYLH5CS","created_at":"2026-07-05T07:06:44.371705+00:00"},{"alias_kind":"pith_short_16","alias_value":"P4PBVBYLH5CSVBYL","created_at":"2026-07-05T07:06:44.371705+00:00"},{"alias_kind":"pith_short_8","alias_value":"P4PBVBYL","created_at":"2026-07-05T07:06:44.371705+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.24832","citing_title":"How much do language models memorize?","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P4PBVBYLH5CSVBYLLIPH233S5B","json":"https://pith.science/pith/P4PBVBYLH5CSVBYLLIPH233S5B.json","graph_json":"https://pith.science/api/pith-number/P4PBVBYLH5CSVBYLLIPH233S5B/graph.json","events_json":"https://pith.science/api/pith-number/P4PBVBYLH5CSVBYLLIPH233S5B/events.json","paper":"https://pith.science/paper/P4PBVBYL"},"agent_actions":{"view_html":"https://pith.science/pith/P4PBVBYLH5CSVBYLLIPH233S5B","download_json":"https://pith.science/pith/P4PBVBYLH5CSVBYLLIPH233S5B.json","view_paper":"https://pith.science/paper/P4PBVBYL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.18988&json=true","fetch_graph":"https://pith.science/api/pith-number/P4PBVBYLH5CSVBYLLIPH233S5B/graph.json","fetch_events":"https://pith.science/api/pith-number/P4PBVBYLH5CSVBYLLIPH233S5B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P4PBVBYLH5CSVBYLLIPH233S5B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P4PBVBYLH5CSVBYLLIPH233S5B/action/storage_attestation","attest_author":"https://pith.science/pith/P4PBVBYLH5CSVBYLLIPH233S5B/action/author_attestation","sign_citation":"https://pith.science/pith/P4PBVBYLH5CSVBYLLIPH233S5B/action/citation_signature","submit_replication":"https://pith.science/pith/P4PBVBYLH5CSVBYLLIPH233S5B/action/replication_record"}},"created_at":"2026-07-05T07:06:44.371705+00:00","updated_at":"2026-07-05T07:06:44.371705+00:00"}