{"as_of":"2026-08-18T08:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:02190cd1e457d307df7533afc71792f9ad38933fd800a7417e3388d2c09fcb41","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:08:47.316990Z","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":"2026-07-02T16:17:08.335369Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.09940","last_updated":"2024-10-21T14:36:35Z","snapshot_observed_at":"2026-08-17T00:20:48.264194Z","submitted_at":"2024-10-13T17:51:21Z","title":"Generalized Group Data Attribution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09940","snapshot_observed_at":"2026-08-16T11:08:47.316990Z","title":"The ﬂan collec tion: Designing data and methods for effective instruction tuning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.16430","last_updated":"2025-04-23T05:32:37Z","snapshot_observed_at":"2026-08-18T07:07:50.665126Z","submitted_at":"2025-04-23T05:32:37Z","title":"MAGIC: Near-Optimal Data Attribution for Deep Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T11:08:47.316990Z"},"links":{"cited_paper":"/paper/2410.09940","citing_paper":"/paper/2504.16430"},"observation_digest":"sha256:8952e9ee2ce7434b5cb6f0d9a6494d06a5d1d380e63bc1842f9ef0291bd52667","observation_id":"94046e50-e258-4b4c-a1cf-21f323b5c526","resolution":{"observed_at":"2026-08-16T11:08:47.316990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09940","last_updated":"2024-10-21T14:36:35Z","snapshot_observed_at":"2026-08-17T00:20:48.264194Z","submitted_at":"2024-10-13T17:51:21Z","title":"Generalized Group Data Attribution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09940","snapshot_observed_at":"2026-08-15T21:34:17.201674Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09603","last_updated":"2026-06-19T22:39:39Z","snapshot_observed_at":"2026-08-17T16:55:32.481777Z","submitted_at":"2025-05-14T17:55:10Z","title":"DataMIL: Selecting Data for Robot Imitation Learning with Datamodels","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T21:34:17.201674Z"},"links":{"cited_paper":"/paper/2410.09940","citing_paper":"/paper/2505.09603"},"observation_digest":"sha256:0ce5abc7ceed9c671d0e5e729d875c3f9211866bab6a1fe21166ef7425884026","observation_id":"af4a7a52-e96a-4f52-921c-27d0b2322970","resolution":{"observed_at":"2026-08-15T21:34:17.201674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09940","last_updated":"2024-10-21T14:36:35Z","snapshot_observed_at":"2026-08-17T00:20:48.264194Z","submitted_at":"2024-10-13T17:51:21Z","title":"Generalized Group Data Attribution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09940","snapshot_observed_at":"2026-08-07T12:17:48.330063Z","title":"doi: 10.18653/v1/2024.acl-long.834","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00175","last_updated":"2026-05-29T03:52:53Z","snapshot_observed_at":"2026-08-14T23:19:02.145292Z","submitted_at":"2025-05-30T19:27:39Z","title":"Who Gets Credit or Blame? Attributing Accountability in Modern AI Systems","version":5},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:17:48.330063Z"},"links":{"cited_paper":"/paper/2410.09940","citing_paper":"/paper/2506.00175"},"observation_digest":"sha256:43ca7f6209132c234fe6310840707cbcccce6f03f90f455b23a75db022c38776","observation_id":"63f2d4b0-cecc-4865-8e92-f39cbabf2753","resolution":{"observed_at":"2026-08-07T12:17:48.330063Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09940","last_updated":"2024-10-21T14:36:35Z","snapshot_observed_at":"2026-08-17T00:20:48.264194Z","submitted_at":"2024-10-13T17:51:21Z","title":"Generalized Group Data Attribution","version":2},"cited_work":{"arxiv_id":"2410.09940","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.09940","snapshot_observed_at":"2026-07-02T16:17:08.335369Z","title":"Generalized group data attribution, 2024","venue":null,"work_id":"6ade2f75-6ba9-4e80-9966-a8d4d768b18b","year":2024},"citing_paper":{"arxiv_id":"2607.00510","last_updated":"2026-07-01T06:45:02Z","snapshot_observed_at":"2026-08-12T12:29:47.497334Z","submitted_at":"2026-07-01T06:45:02Z","title":"Prototype Language Models","version":1},"reference_index":173,"source":"arxiv_source","source_observed_at":"2026-07-02T16:13:43.039645Z"},"links":{"cited_paper":"/paper/2410.09940","citing_paper":"/paper/2607.00510"},"observation_digest":"sha256:085a41d9a937bc1925e8cf14097ce920aa51dc3cbccba8840fdab9d386cfdb11","observation_id":"10963ca6-4187-4db3-bf05-9c3c3967d30f","resolution":{"observed_at":"2026-07-02T16:17:08.337034Z","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/2410.09940/citation-record","integrity":"/paper/2410.09940/integrity","json":"/paper/2410.09940/citation-record.json","paper":"/paper/2410.09940"},"outbound":[],"paper":{"arxiv_id":"2410.09940","last_updated":"2024-10-21T14:36:35Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T00:20:48.264194Z","submitted_at":"2024-10-13T17:51:21Z","title":"Generalized Group Data Attribution"},"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 4 inbound Pith citation observations for arXiv:2410.09940."}