{"as_of":"2026-08-09T16:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:de95479474d4cb0a0ba700a9c5786a2d3ad43ccb60ce021cb6d49fd0c63d2b97","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":"2026-08-09T06:31:02.800959+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":"2026-08-07T14:44:34.737273Z","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":"2026-08-07T14:44:37.557080Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.00612","last_updated":"2025-05-29T01:48:23Z","snapshot_observed_at":"2026-08-07T15:57:20.674591Z","submitted_at":"2025-05-01T15:43:51Z","title":"Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation","version":2},"cited_work":{"arxiv_id":"2505.00612","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.00612","snapshot_observed_at":"2026-08-07T14:44:37.557080Z","title":"Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation","venue":"cs.AI","work_id":"aa725110-d9a5-4aeb-806a-54db655e288c","year":2025},"citing_paper":{"arxiv_id":"2505.18102","last_updated":"2026-05-30T13:29:55Z","snapshot_observed_at":"2026-08-07T14:33:15.240605Z","submitted_at":"2025-05-23T16:57:34Z","title":"CapBencher: Give Your LLM Benchmark a Built-in Alarm for Test-Set Overfitting","version":7},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T14:44:34.737273Z"},"links":{"cited_paper":"/paper/2505.00612","citing_paper":"/paper/2505.18102"},"observation_digest":"sha256:8c3191e60095140a8b8f22bc57e7ed3b972c5b9c7c2e3f86e4dd9a507efba257","observation_id":"fd3fd3f4-6ca8-4869-8edb-d911de07a0e1","resolution":{"observed_at":"2026-08-07T14:44:37.671156Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.00612","last_updated":"2025-05-29T01:48:23Z","snapshot_observed_at":"2026-08-07T15:57:20.674591Z","submitted_at":"2025-05-01T15:43:51Z","title":"Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.00612","snapshot_observed_at":"2026-08-01T16:19:08.637388Z","title":"Sculley, Will Cukierski, Phil Culliton, Sohier Dane, Maggie Demkin, Ryan Holbrook, Addison Howard, Paul Mooney, Walter Reade, Megan Risdal, and Nate Keating","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.18063","last_updated":"2026-07-20T15:30:38Z","snapshot_observed_at":"2026-08-07T18:42:59.875513Z","submitted_at":"2026-07-20T15:30:38Z","title":"Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T16:19:08.637388Z"},"links":{"cited_paper":"/paper/2505.00612","citing_paper":"/paper/2607.18063"},"observation_digest":"sha256:08e90094c9cec40347ff966fca5a8773724615f1e86b9552db38d269ace98d4b","observation_id":"d47b433a-ecdd-4c60-bf5f-c86b2e713985","resolution":{"observed_at":"2026-08-01T16:19:08.637388Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2505.00612/citation-record","integrity":"/paper/2505.00612/integrity","json":"/paper/2505.00612/citation-record.json","paper":"/paper/2505.00612"},"outbound":[],"paper":{"arxiv_id":"2505.00612","last_updated":"2025-05-29T01:48:23Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-07T15:57:20.674591Z","submitted_at":"2025-05-01T15:43:51Z","title":"Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2505.00612."}