{"as_of":"2026-08-08T12:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:17043a53e81d8849b11d3f1f42583b768980710d151622aea87a5df09b7bb0a5","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":"2026-08-08T06:32:00.761636+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":"2026-08-07T13:59:49.210447Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.17646","last_updated":"2024-12-23T15:21:50Z","snapshot_observed_at":"2026-07-06T20:12:13.548098Z","submitted_at":"2024-12-23T15:21:50Z","title":"Rate of Model Collapse in Recursive Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17646","snapshot_observed_at":"2026-08-07T13:59:49.210447Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.15690","last_updated":"2025-07-24T05:08:02Z","snapshot_observed_at":"2026-08-07T13:50:02.521930Z","submitted_at":"2025-05-26T22:10:52Z","title":"LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T13:59:49.210447Z"},"links":{"cited_paper":"/paper/2412.17646","citing_paper":"/paper/2506.15690"},"observation_digest":"sha256:531ec8048fab47a7bbea1ed74104759e1100981912cd77729cbe168bf288d09e","observation_id":"d3feed96-cf1b-4aff-b7b3-ee260e477945","resolution":{"observed_at":"2026-08-07T13:59:49.210447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17646","last_updated":"2024-12-23T15:21:50Z","snapshot_observed_at":"2026-07-06T20:12:13.548098Z","submitted_at":"2024-12-23T15:21:50Z","title":"Rate of Model Collapse in Recursive Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17646","snapshot_observed_at":"2026-08-02T22:51:31.140572Z","title":"Rate of model collapse in recursive training.arXiv preprint arXiv:2412.17646,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.16065","last_updated":"2026-06-26T00:44:59Z","snapshot_observed_at":"2026-08-08T10:24:22.445959Z","submitted_at":"2026-02-17T22:38:18Z","title":"Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T22:51:31.140572Z"},"links":{"cited_paper":"/paper/2412.17646","citing_paper":"/paper/2602.16065"},"observation_digest":"sha256:46eb5cabeb2a9c2e1eb668ab8afdf28ba18641fff64bdcb3486e5bc8e42e434e","observation_id":"855e7720-495d-4353-bca4-bbb0968c09e4","resolution":{"observed_at":"2026-08-02T22:51:31.140572Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17646","last_updated":"2024-12-23T15:21:50Z","snapshot_observed_at":"2026-07-06T20:12:13.548098Z","submitted_at":"2024-12-23T15:21:50Z","title":"Rate of Model Collapse in Recursive Training","version":1},"cited_work":{"arxiv_id":"2412.17646","doi":"10.48550/arxiv.2412.17646","metadata_source":"arxiv_reference","pith_arxiv_id":"2412.17646","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Proceedings of the 28th International Conference on Artificial Intelligence and Statistics , year =","venue":"arXiv (Cornell University)","work_id":"61d207a0-61f7-45b3-9307-68f79f4f10a1","year":null},"citing_paper":{"arxiv_id":"2606.28438","last_updated":"2026-06-26T07:35:43Z","snapshot_observed_at":"2026-08-03T00:04:55.169727Z","submitted_at":"2026-06-26T07:35:43Z","title":"When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs","version":1},"reference_index":153,"source":"arxiv_source","source_observed_at":"2026-06-30T01:29:42.919461Z"},"links":{"cited_paper":"/paper/2412.17646","citing_paper":"/paper/2606.28438"},"observation_digest":"sha256:d6e310d14a97fb87aaf7e22b76751f1bd59c643599dd7d6792eddcf4827b17ab","observation_id":"03038d9a-2be1-4464-9476-8047b8c7a150","resolution":{"observed_at":"2026-06-30T01:34:09.480756Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.17646/citation-record","integrity":"/paper/2412.17646/integrity","json":"/paper/2412.17646/citation-record.json","paper":"/paper/2412.17646"},"outbound":[],"paper":{"arxiv_id":"2412.17646","last_updated":"2024-12-23T15:21:50Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T20:12:13.548098Z","submitted_at":"2024-12-23T15:21:50Z","title":"Rate of Model Collapse in Recursive Training"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2412.17646."}