{"as_of":"2026-08-12T03:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3029393626f98e0ffa042d51047a94d24676e44b15d6d9766868a614e35e1c3b","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":"2026-08-11T06:34:44.6726+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":"2026-08-07T14:31:48.050210Z","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":"2026-08-06T16:34:26.014981Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.13131","last_updated":"2025-06-11T05:43:13Z","snapshot_observed_at":"2026-08-10T09:50:08.783407Z","submitted_at":"2025-02-18T18:55:26Z","title":"Rethinking Diverse Human Preference Learning through Principal Component Analysis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13131","snapshot_observed_at":"2026-08-07T14:31:48.050210Z","title":"Rethinking diverse human preference learning through principal component analysis","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.21537","last_updated":"2025-05-24T09:07:13Z","snapshot_observed_at":"2026-08-08T02:37:32.325481Z","submitted_at":"2025-05-24T09:07:13Z","title":"OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models","version":1},"reference_index":152,"source":"arxiv_source","source_observed_at":"2026-08-07T14:31:48.050210Z"},"links":{"cited_paper":"/paper/2502.13131","citing_paper":"/paper/2505.21537"},"observation_digest":"sha256:2e128b1993e38a5995613d93be2d50d7beb02e1d22e8798f7ecdcaeb9a3a7615","observation_id":"aa3dae91-b5dd-4f5e-bf93-8c62f453417a","resolution":{"observed_at":"2026-08-07T14:31:48.050210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13131","last_updated":"2025-06-11T05:43:13Z","snapshot_observed_at":"2026-08-10T09:50:08.783407Z","submitted_at":"2025-02-18T18:55:26Z","title":"Rethinking Diverse Human Preference Learning through Principal Component Analysis","version":2},"cited_work":{"arxiv_id":"2502.13131","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.13131","snapshot_observed_at":"2026-08-06T16:34:26.014981Z","title":"Rethinking Diverse Human Preference Learning through Principal Component Analysis","venue":"cs.AI","work_id":"175e89d2-e0a7-4323-9212-040ec0bce92d","year":2025},"citing_paper":{"arxiv_id":"2507.13158","last_updated":"2025-07-17T14:22:24Z","snapshot_observed_at":"2026-08-09T20:19:53.961381Z","submitted_at":"2025-07-17T14:22:24Z","title":"Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T16:34:25.104669Z"},"links":{"cited_paper":"/paper/2502.13131","citing_paper":"/paper/2507.13158"},"observation_digest":"sha256:0da7ff67db99b8e6cf57332de94596c2e9baae8285a82c60d06066dfac006abb","observation_id":"28372cfb-437c-4cf6-9e3c-4a6219d7a720","resolution":{"observed_at":"2026-08-06T16:34:26.018857Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.13131/citation-record","integrity":"/paper/2502.13131/integrity","json":"/paper/2502.13131/citation-record.json","paper":"/paper/2502.13131"},"outbound":[],"paper":{"arxiv_id":"2502.13131","last_updated":"2025-06-11T05:43:13Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-10T09:50:08.783407Z","submitted_at":"2025-02-18T18:55:26Z","title":"Rethinking Diverse Human Preference Learning through Principal Component Analysis"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2502.13131."}