{"as_of":"2026-08-18T10:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b9cfd9d4ae2a0538854c9d9b14257bcc8aea0f30ada8e7b23f46bb99fe4f5b4d","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:39:38.993799Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-16T07:00:43.267354Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.19919","last_updated":"2024-07-15T12:07:03Z","snapshot_observed_at":"2026-08-18T07:54:27.354660Z","submitted_at":"2023-10-30T18:29:26Z","title":"Meta-Learning Strategies through Value Maximization in Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.19919","snapshot_observed_at":"2026-08-06T18:39:38.993799Z","title":"Meta-learning strategies through value maximization in neural networks.arXiv preprint arXiv:2310.19919, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.07907","last_updated":"2026-06-22T15:49:10Z","snapshot_observed_at":"2026-08-16T15:37:35.648099Z","submitted_at":"2025-07-10T16:39:46Z","title":"A statistical physics framework for optimal learning","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T18:39:38.993799Z"},"links":{"cited_paper":"/paper/2310.19919","citing_paper":"/paper/2507.07907"},"observation_digest":"sha256:4ff43697baccc7bf05d42d3f7a20c5366befd851112af91644fe65e803750b0b","observation_id":"3ad444c6-b75d-4f91-a3f6-00d765b92091","resolution":{"observed_at":"2026-08-06T18:39:38.993799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.19919","last_updated":"2024-07-15T12:07:03Z","snapshot_observed_at":"2026-08-18T07:54:27.354660Z","submitted_at":"2023-10-30T18:29:26Z","title":"Meta-Learning Strategies through Value Maximization in Neural Networks","version":2},"cited_work":{"arxiv_id":"2310.19919","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.19919","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"7697f908-62d0-4a68-a10c-ba1eeb5bf205","year":null},"citing_paper":{"arxiv_id":"2602.04774","last_updated":"2026-05-08T16:24:57Z","snapshot_observed_at":"2026-08-15T04:58:48.280086Z","submitted_at":"2026-02-04T17:11:36Z","title":"Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-16T06:58:38.927268Z"},"links":{"cited_paper":"/paper/2310.19919","citing_paper":"/paper/2602.04774"},"observation_digest":"sha256:7a1cbfd72aaaaf6f2dafd417b26eacb675832c490c3db18645bd69e099d7c3c3","observation_id":"0aa5c705-056b-4661-944a-9c8beca525ad","resolution":{"observed_at":"2026-05-16T07:00:43.268950Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2310.19919/citation-record","integrity":"/paper/2310.19919/integrity","json":"/paper/2310.19919/citation-record.json","paper":"/paper/2310.19919"},"outbound":[],"paper":{"arxiv_id":"2310.19919","last_updated":"2024-07-15T12:07:03Z","latest_version":2,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-18T07:54:27.354660Z","submitted_at":"2023-10-30T18:29:26Z","title":"Meta-Learning Strategies through Value Maximization in Neural Networks"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.19919."}