{"as_of":"2026-08-05T00:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a88897ce9463d51f65c8b91c3dc2ea6df22463cadb74cb13f46348a1d9af877c","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T23:43:05.246094Z","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":"2026-07-04T08:39:41.704212Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2202.06417","last_updated":"2022-09-26T14:59:44Z","snapshot_observed_at":"2026-07-06T12:37:21.762051Z","submitted_at":"2022-02-13T21:46:14Z","title":"A Contrastive Framework for Neural Text Generation","version":3},"cited_work":{"arxiv_id":"2202.06417","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2202.06417","snapshot_observed_at":"2026-07-04T08:39:41.704212Z","title":"arXiv preprint arXiv:2202.06417 , year=","venue":null,"work_id":"49989ddc-2f27-4c7b-bb14-e2f3ad2eeea3","year":2022},"citing_paper":{"arxiv_id":"2304.06767","last_updated":"2023-12-01T14:28:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-04-13T18:22:40Z","title":"RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment","version":4},"reference_index":140,"source":"arxiv_source","source_observed_at":"2026-05-18T00:46:56.664582Z"},"links":{"cited_paper":"/paper/2202.06417","citing_paper":"/paper/2304.06767"},"observation_digest":"sha256:9fd9464d20a2ea948ea52e2a2758a005efec0ef59eb82faf56b0d8f3cd019901","observation_id":"1015a749-2656-461a-950f-ebc057efbcfa","resolution":{"observed_at":"2026-05-18T00:46:56.877390Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.06417","last_updated":"2022-09-26T14:59:44Z","snapshot_observed_at":"2026-07-06T12:37:21.762051Z","submitted_at":"2022-02-13T21:46:14Z","title":"A Contrastive Framework for Neural Text Generation","version":3},"cited_work":{"arxiv_id":"2202.06417","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2202.06417","snapshot_observed_at":"2026-07-04T08:39:41.704212Z","title":"arXiv preprint arXiv:2202.06417 , year=","venue":null,"work_id":"49989ddc-2f27-4c7b-bb14-e2f3ad2eeea3","year":2022},"citing_paper":{"arxiv_id":"2606.22296","last_updated":"2026-06-21T01:41:35Z","snapshot_observed_at":"2026-08-02T18:34:13.610197Z","submitted_at":"2026-06-21T01:41:35Z","title":"SCENIC: Semantic-Conditioned Edge-Aware Neural Framework for Structured IoT Command Generation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T11:21:51.750330Z"},"links":{"cited_paper":"/paper/2202.06417","citing_paper":"/paper/2606.22296"},"observation_digest":"sha256:8171c411db7beff074c2f7306094a2242d08310c66fa7ed350c16dfb698cb0b7","observation_id":"596b5a7a-c4cb-42c3-b6ae-2b1f7d302f80","resolution":{"observed_at":"2026-07-04T08:39:41.705723Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.06417","last_updated":"2022-09-26T14:59:44Z","snapshot_observed_at":"2026-07-06T12:37:21.762051Z","submitted_at":"2022-02-13T21:46:14Z","title":"A Contrastive Framework for Neural Text Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.06417","snapshot_observed_at":"2026-08-01T23:43:05.246094Z","title":"2202.06417 , archiveprefix =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.15277","last_updated":"2026-07-16T17:59:31Z","snapshot_observed_at":"2026-08-03T17:08:01.241612Z","submitted_at":"2026-07-16T17:59:31Z","title":"Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-01T23:43:05.246094Z"},"links":{"cited_paper":"/paper/2202.06417","citing_paper":"/paper/2607.15277"},"observation_digest":"sha256:20c86f1d1ef2f61444971813c765e71d7e3ef4c5d6f153b3415d001461348d35","observation_id":"d06e1550-9b25-4512-80e0-a60c39147a3a","resolution":{"observed_at":"2026-08-01T23:43:05.246094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2202.06417/citation-record","integrity":"/paper/2202.06417/integrity","json":"/paper/2202.06417/citation-record.json","paper":"/paper/2202.06417"},"outbound":[],"paper":{"arxiv_id":"2202.06417","last_updated":"2022-09-26T14:59:44Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T12:37:21.762051Z","submitted_at":"2022-02-13T21:46:14Z","title":"A Contrastive Framework for Neural Text Generation"},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2202.06417."}