{"as_of":"2026-08-20T02:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4ed915d00de63cace0f9882f5f30052140581c9339394a376226f774620ee115","coverage":[{"denominator":2,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T09:22:55.693589Z","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":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T05:56:17.737907Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2601.14032","last_updated":"2026-07-26T05:30:00Z","snapshot_observed_at":"2026-08-15T11:38:52.276351Z","submitted_at":"2026-01-20T14:53:32Z","title":"RM-Distiller: Exploiting Generative LLM for Reward Model Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.14032","snapshot_observed_at":"2026-08-04T05:56:17.737907Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.08091","last_updated":"2026-08-01T14:08:22Z","snapshot_observed_at":"2026-08-18T18:58:51.956325Z","submitted_at":"2026-03-09T08:32:21Z","title":"Toward Robust LLM-Based Judges: Taxonomic Bias Evaluation and Debiasing Optimization","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T05:56:17.737907Z"},"links":{"cited_paper":"/paper/2601.14032","citing_paper":"/paper/2603.08091"},"observation_digest":"sha256:9dd8dcbf1ff0b70d8c2c9b0e3ddfff359dea2fbd444577cd7da4347226f6d2f2","observation_id":"9a773957-8649-4b9b-ad69-1914364fe1e5","resolution":{"observed_at":"2026-08-04T05:56:17.737907Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2601.14032/citation-record","integrity":"/paper/2601.14032/integrity","json":"/paper/2601.14032/citation-record.json","paper":"/paper/2601.14032"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T09:22:55.562956Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.14032","last_updated":"2026-07-26T05:30:00Z","snapshot_observed_at":"2026-08-15T11:38:52.276351Z","submitted_at":"2026-01-20T14:53:32Z","title":"RM-Distiller: Exploiting Generative LLM for Reward Model Distillation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T09:22:55.562956Z"},"links":{"citing_paper":"/paper/2601.14032"},"observation_digest":"sha256:99a61725b36a57f78fa301bebd666b14f145db395755bd4fc155af1e9402e498","observation_id":"45c36267-7c52-478e-99cd-8facc28a1d18","resolution":{"observed_at":"2026-08-03T09:22:55.562956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T09:22:55.693589Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.14032","last_updated":"2026-07-26T05:30:00Z","snapshot_observed_at":"2026-08-15T11:38:52.276351Z","submitted_at":"2026-01-20T14:53:32Z","title":"RM-Distiller: Exploiting Generative LLM for Reward Model Distillation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T09:22:55.693589Z"},"links":{"citing_paper":"/paper/2601.14032"},"observation_digest":"sha256:2405593ff944e110fe2939fcb1b7b19db680f48339204e221fee150888f782d3","observation_id":"8adebea6-7fb7-46de-b6df-78a16cd9cf67","resolution":{"observed_at":"2026-08-03T09:22:55.693589Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2601.14032","last_updated":"2026-07-26T05:30:00Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-15T11:38:52.276351Z","submitted_at":"2026-01-20T14:53:32Z","title":"RM-Distiller: Exploiting Generative LLM for Reward Model Distillation"},"reference_resolution":{"displayed":2,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":2},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 1 inbound Pith citation observation for arXiv:2601.14032."}