{"as_of":"2026-08-10T15:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:88c596866559516b1100b4eb10d202cd6006db4ee10327d4e0fd227f1b596bed","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":"2026-08-10T06:31:04.303077+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":"2026-08-07T10:23:06.239553Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T00:56:50.630303Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.19572","last_updated":"2023-10-30T14:29:41Z","snapshot_observed_at":"2026-08-10T13:00:05.347456Z","submitted_at":"2023-10-30T14:29:41Z","title":"Improving Input-label Mapping with Demonstration Replay for In-context Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.19572","snapshot_observed_at":"2026-08-07T10:23:06.239553Z","title":"Improving input-label mapping with demonstration replay for in-context learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05584","last_updated":"2025-06-05T20:59:33Z","snapshot_observed_at":"2026-08-10T01:04:11.251337Z","submitted_at":"2025-06-05T20:59:33Z","title":"TabFlex: Scaling Tabular Learning to Millions with Linear Attention","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T10:23:06.239553Z"},"links":{"cited_paper":"/paper/2310.19572","citing_paper":"/paper/2506.05584"},"observation_digest":"sha256:c7c2bbd95c292bf0bf5bedcbe75220d2da13d0029f407a5bf45198f5ff4efd61","observation_id":"51a8d04a-da06-4925-a6d1-77bc7f94aec8","resolution":{"observed_at":"2026-08-07T10:23:06.239553Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.19572","last_updated":"2023-10-30T14:29:41Z","snapshot_observed_at":"2026-08-10T13:00:05.347456Z","submitted_at":"2023-10-30T14:29:41Z","title":"Improving Input-label Mapping with Demonstration Replay for In-context Learning","version":1},"cited_work":{"arxiv_id":"2310.19572","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.19572","snapshot_observed_at":"2026-08-07T00:56:50.630303Z","title":"Improving Input-label Mapping with Demonstration Replay for In-context Learning","venue":"cs.CL","work_id":"56e7de14-6898-48bc-bf81-54704ed9bb42","year":2023},"citing_paper":{"arxiv_id":"2506.12346","last_updated":"2025-06-14T04:51:34Z","snapshot_observed_at":"2026-08-10T13:00:05.945208Z","submitted_at":"2025-06-14T04:51:34Z","title":"Refract ICL: Rethinking Example Selection in the Era of Million-Token Models","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T00:56:50.462984Z"},"links":{"cited_paper":"/paper/2310.19572","citing_paper":"/paper/2506.12346"},"observation_digest":"sha256:cda5e2680afc5e09bcc03ad855930da8e441370ad3b3232feacfb27e6cd1347c","observation_id":"300888b0-6ea0-4a90-8a12-718283948bb6","resolution":{"observed_at":"2026-08-07T00:56:50.633917Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2310.19572/citation-record","integrity":"/paper/2310.19572/integrity","json":"/paper/2310.19572/citation-record.json","paper":"/paper/2310.19572"},"outbound":[],"paper":{"arxiv_id":"2310.19572","last_updated":"2023-10-30T14:29:41Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-10T13:00:05.347456Z","submitted_at":"2023-10-30T14:29:41Z","title":"Improving Input-label Mapping with Demonstration Replay for In-context Learning"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.19572."}