{"as_of":"2026-08-05T17:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5c660e7bdda620fc0af6a6faaf2c3af412ebce7e02c6afb9b964367bf23bc8ec","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T16:02:25.948964Z","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":"2026-05-23T06:25:27.821937Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.14872","last_updated":"2024-10-22T22:18:14Z","snapshot_observed_at":"2026-07-06T19:36:15.200874Z","submitted_at":"2024-10-18T21:38:21Z","title":"How to Evaluate Reward Models for RLHF","version":2},"cited_work":{"arxiv_id":"2410.14872","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.14872","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Athene-70b: Redefining the boundaries of post-training for open models, July 2024a","venue":null,"work_id":"f3a6faaf-bc50-44dd-9f4e-e822937f1d66","year":2024},"citing_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-23T06:25:00.376073Z"},"links":{"cited_paper":"/paper/2410.14872","citing_paper":"/paper/2412.15115"},"observation_digest":"sha256:13396a95d5c81708fb5ab7948b13afeb4976aacfc9b4b81f5ee0094387bb3acf","observation_id":"1be5e7df-2b31-4e5b-af58-5138884f6000","resolution":{"observed_at":"2026-05-23T06:25:27.824781Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14872","last_updated":"2024-10-22T22:18:14Z","snapshot_observed_at":"2026-07-06T19:36:15.200874Z","submitted_at":"2024-10-18T21:38:21Z","title":"How to Evaluate Reward Models for RLHF","version":2},"cited_work":{"arxiv_id":"2410.14872","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.14872","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Athene-70b: Redefining the boundaries of post-training for open models, July 2024a","venue":null,"work_id":"f3a6faaf-bc50-44dd-9f4e-e822937f1d66","year":2024},"citing_paper":{"arxiv_id":"2506.01937","last_updated":"2026-04-23T14:42:19Z","snapshot_observed_at":"2026-07-06T21:35:12.872021Z","submitted_at":"2025-06-02T17:54:04Z","title":"RewardBench 2: Advancing Reward Model Evaluation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-19T11:18:03.965711Z"},"links":{"cited_paper":"/paper/2410.14872","citing_paper":"/paper/2506.01937"},"observation_digest":"sha256:9787661d3cc71d7a9e1747e1fc6cb610745fa4c224d61e51b7a3f863e92a997c","observation_id":"32242d9b-6592-4ddc-8418-e2dfbe177a3e","resolution":{"observed_at":"2026-05-19T11:22:16.817153Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14872","last_updated":"2024-10-22T22:18:14Z","snapshot_observed_at":"2026-07-06T19:36:15.200874Z","submitted_at":"2024-10-18T21:38:21Z","title":"How to Evaluate Reward Models for RLHF","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14872","snapshot_observed_at":"2026-08-05T16:02:25.948964Z","title":"N.; Jiao, J.; Zhu, B.; Gonzalez, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19132","last_updated":"2025-08-26T15:34:17Z","snapshot_observed_at":"2026-08-05T16:02:11.446295Z","submitted_at":"2025-08-26T15:34:17Z","title":"Active Query Selection for Crowd-Based Reinforcement Learning","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-05T16:02:25.948964Z"},"links":{"cited_paper":"/paper/2410.14872","citing_paper":"/paper/2508.19132"},"observation_digest":"sha256:22c4de769f1abc1a18d9f5c5f2cf1d878cc33517eb73ac29458eabfa3e310413","observation_id":"54c68ee6-2f9c-4abb-964f-6da8ef9c2e91","resolution":{"observed_at":"2026-08-05T16:02:25.948964Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14872","last_updated":"2024-10-22T22:18:14Z","snapshot_observed_at":"2026-07-06T19:36:15.200874Z","submitted_at":"2024-10-18T21:38:21Z","title":"How to Evaluate Reward Models for RLHF","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.14872","snapshot_observed_at":"2026-08-03T08:15:17.876896Z","title":"Angelopoulos, Jiantao Jiao, Banghua Zhu, Joseph E","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.17717","last_updated":"2026-06-09T20:25:14Z","snapshot_observed_at":"2026-08-03T08:15:07.553014Z","submitted_at":"2026-01-25T06:40:25Z","title":"A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data","version":3},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-03T08:15:17.876896Z"},"links":{"cited_paper":"/paper/2410.14872","citing_paper":"/paper/2601.17717"},"observation_digest":"sha256:31abbda95bce2f3efd764c892497bdbf43472e816b79c9392ce949276b167d9d","observation_id":"03dbc1b8-32d0-41cb-8622-38e8d95efb1e","resolution":{"observed_at":"2026-08-03T08:15:17.876896Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.14872","last_updated":"2024-10-22T22:18:14Z","snapshot_observed_at":"2026-07-06T19:36:15.200874Z","submitted_at":"2024-10-18T21:38:21Z","title":"How to Evaluate Reward Models for RLHF","version":2},"cited_work":{"arxiv_id":"2410.14872","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.14872","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Athene-70b: Redefining the boundaries of post-training for open models, July 2024a","venue":null,"work_id":"f3a6faaf-bc50-44dd-9f4e-e822937f1d66","year":2024},"citing_paper":{"arxiv_id":"2604.12312","last_updated":"2026-04-14T05:42:41Z","snapshot_observed_at":"2026-08-02T05:56:57.097541Z","submitted_at":"2026-04-14T05:42:41Z","title":"CompliBench: Benchmarking LLM Judges for Compliance Violation Detection in Dialogue Systems","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T14:56:00.449776Z"},"links":{"cited_paper":"/paper/2410.14872","citing_paper":"/paper/2604.12312"},"observation_digest":"sha256:3cc20be15d34ea159a2f24652b8948ed6e60ea42ccfd6825f5697e8efc0c4f5d","observation_id":"195b989d-6a45-47a2-9d0e-40a5a1bab2c3","resolution":{"observed_at":"2026-05-11T11:26:02.353928Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2410.14872/citation-record","integrity":"/paper/2410.14872/integrity","json":"/paper/2410.14872/citation-record.json","paper":"/paper/2410.14872"},"outbound":[],"paper":{"arxiv_id":"2410.14872","last_updated":"2024-10-22T22:18:14Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T19:36:15.200874Z","submitted_at":"2024-10-18T21:38:21Z","title":"How to Evaluate Reward Models for RLHF"},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.14872."}