{"as_of":"2026-08-22T10:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c6fadbdc00326c948e6586af184af519b819dd93cf6e47f624abc94542fc2f10","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":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T00:21:59.244898Z","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":"2026-08-05T13:20:39.606465Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.12086","last_updated":"2024-10-25T12:04:26Z","snapshot_observed_at":"2026-08-21T19:37:54.327369Z","submitted_at":"2024-01-22T16:24:43Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12086","snapshot_observed_at":"2026-08-12T20:34:03.843013Z","title":"liar, liar pants on fire","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.09547","last_updated":"2024-12-12T19:23:28Z","snapshot_observed_at":"2026-08-17T06:46:16.591020Z","submitted_at":"2024-11-14T16:01:33Z","title":"Piecing It All Together: Verifying Multi-Hop Multimodal Claims","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-12T20:34:03.843013Z"},"links":{"cited_paper":"/paper/2401.12086","citing_paper":"/paper/2411.09547"},"observation_digest":"sha256:0254a905182b2e114ec55bb9479914eb9a005749daab79880406a5bc918231a2","observation_id":"c40431ed-2060-4019-bc40-aef5516d718d","resolution":{"observed_at":"2026-08-12T20:34:03.843013Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12086","last_updated":"2024-10-25T12:04:26Z","snapshot_observed_at":"2026-08-21T19:37:54.327369Z","submitted_at":"2024-01-22T16:24:43Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12086","snapshot_observed_at":"2026-08-12T12:58:30.027648Z","title":"West-of-n: Synthetic preference generation for improved reward modeling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16646","last_updated":"2025-02-09T07:53:38Z","snapshot_observed_at":"2026-08-13T05:48:57.895519Z","submitted_at":"2024-11-25T18:28:26Z","title":"Self-Generated Critiques Boost Reward Modeling for Language Models","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-12T12:58:30.027648Z"},"links":{"cited_paper":"/paper/2401.12086","citing_paper":"/paper/2411.16646"},"observation_digest":"sha256:81067cef8b0f95d0a9232e354a235ddcdc5c50d4ffc948795044dc9938948d48","observation_id":"f83f81e4-53fb-4b58-bc24-dc87258681b0","resolution":{"observed_at":"2026-08-12T12:58:30.027648Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12086","last_updated":"2024-10-25T12:04:26Z","snapshot_observed_at":"2026-08-21T19:37:54.327369Z","submitted_at":"2024-01-22T16:24:43Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12086","snapshot_observed_at":"2026-08-12T00:11:14.744562Z","title":"West-of-n: Synthetic preference generation for improved reward modeling.arXiv:2401.12086,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.01951","last_updated":"2024-12-04T14:20:21Z","snapshot_observed_at":"2026-08-18T01:49:25.380130Z","submitted_at":"2024-12-02T20:24:17Z","title":"Self-Improvement in Language Models: The Sharpening Mechanism","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T00:11:14.744562Z"},"links":{"cited_paper":"/paper/2401.12086","citing_paper":"/paper/2412.01951"},"observation_digest":"sha256:7da9117620a4ec4c95b8056d14274c48ec41b1a663b6364b971bd37cc4300974","observation_id":"5a8d4734-9aa9-4cac-9a4e-e10413807cee","resolution":{"observed_at":"2026-08-12T00:11:14.744562Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12086","last_updated":"2024-10-25T12:04:26Z","snapshot_observed_at":"2026-08-21T19:37:54.327369Z","submitted_at":"2024-01-22T16:24:43Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12086","snapshot_observed_at":"2026-08-11T22:57:01.895819Z","title":"West-of-n: Synthetic preference generation for improved reward modeling, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02980","last_updated":"2024-12-09T22:23:41Z","snapshot_observed_at":"2026-08-15T13:18:49.671776Z","submitted_at":"2024-12-04T02:47:45Z","title":"Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models","version":2},"reference_index":145,"source":"arxiv_source","source_observed_at":"2026-08-11T22:57:01.895819Z"},"links":{"cited_paper":"/paper/2401.12086","citing_paper":"/paper/2412.02980"},"observation_digest":"sha256:940ffcf37cdd853f72c07213fb20aea5867be803acc1bea31d5c28a6a729f9a3","observation_id":"cbaa0a07-b350-4c6d-8652-9aa6c7fa4965","resolution":{"observed_at":"2026-08-11T22:57:01.895819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12086","last_updated":"2024-10-25T12:04:26Z","snapshot_observed_at":"2026-08-21T19:37:54.327369Z","submitted_at":"2024-01-22T16:24:43Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12086","snapshot_observed_at":"2026-08-09T23:02:23.371686Z","title":"West-of-n: Synthetic preference generation for improved reward modeling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18578","last_updated":"2025-02-26T18:58:53Z","snapshot_observed_at":"2026-08-17T15:14:37.183066Z","submitted_at":"2025-01-30T18:50:25Z","title":"R.I.P.: Better Models by Survival of the Fittest Prompts","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-09T23:02:23.371686Z"},"links":{"cited_paper":"/paper/2401.12086","citing_paper":"/paper/2501.18578"},"observation_digest":"sha256:c5a7e250167c280cb7956127071b466096d15cbac1744e203f14d6cb916ed1a5","observation_id":"b00e01a3-5dd9-49aa-8af0-8751ab8a689b","resolution":{"observed_at":"2026-08-09T23:02:23.371686Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12086","last_updated":"2024-10-25T12:04:26Z","snapshot_observed_at":"2026-08-21T19:37:54.327369Z","submitted_at":"2024-01-22T16:24:43Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12086","snapshot_observed_at":"2026-08-15T23:12:38.970109Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.05327","last_updated":"2025-05-18T11:24:09Z","snapshot_observed_at":"2026-08-18T08:02:49.248229Z","submitted_at":"2025-05-08T15:17:37Z","title":"RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-15T23:12:38.970109Z"},"links":{"cited_paper":"/paper/2401.12086","citing_paper":"/paper/2505.05327"},"observation_digest":"sha256:8a2b917803e05384738bc1230b651df363608d5796d9d41e2370f40362bb4efa","observation_id":"41958aec-42c1-4d19-abc4-64c44ef6fdf9","resolution":{"observed_at":"2026-08-15T23:12:38.970109Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12086","last_updated":"2024-10-25T12:04:26Z","snapshot_observed_at":"2026-08-21T19:37:54.327369Z","submitted_at":"2024-01-22T16:24:43Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12086","snapshot_observed_at":"2026-08-15T20:53:39.688797Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06292","last_updated":"2025-06-10T03:32:39Z","snapshot_observed_at":"2026-08-22T03:21:45.953024Z","submitted_at":"2025-05-17T04:34:23Z","title":"Mutual-Taught for Co-adapting Policy and Reward Models","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T20:53:39.688797Z"},"links":{"cited_paper":"/paper/2401.12086","citing_paper":"/paper/2506.06292"},"observation_digest":"sha256:0d9a1e058114d8fc08983e8733bbd8e532798711b288e86098226e1ab4c56bae","observation_id":"50a0298e-512c-4696-87af-53bfcd238469","resolution":{"observed_at":"2026-08-15T20:53:39.688797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12086","last_updated":"2024-10-25T12:04:26Z","snapshot_observed_at":"2026-08-21T19:37:54.327369Z","submitted_at":"2024-01-22T16:24:43Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12086","snapshot_observed_at":"2026-08-06T22:56:33.472641Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20274","last_updated":"2025-06-25T09:34:25Z","snapshot_observed_at":"2026-08-21T16:28:49.359450Z","submitted_at":"2025-06-25T09:34:25Z","title":"Enterprise Large Language Model Evaluation Benchmark","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T22:56:33.472641Z"},"links":{"cited_paper":"/paper/2401.12086","citing_paper":"/paper/2506.20274"},"observation_digest":"sha256:fabcfefbd8019fceaa649d78c732b2d303aa4b452050c81fe264436771bfd74b","observation_id":"139a865c-3c99-47d3-bdc5-87120610f228","resolution":{"observed_at":"2026-08-06T22:56:33.472641Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12086","last_updated":"2024-10-25T12:04:26Z","snapshot_observed_at":"2026-08-21T19:37:54.327369Z","submitted_at":"2024-01-22T16:24:43Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12086","snapshot_observed_at":"2026-08-06T22:28:07.646010Z","title":"West-of-n: Synthetic preference generation for improved reward modeling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.21495","last_updated":"2025-06-26T17:25:49Z","snapshot_observed_at":"2026-08-16T08:59:36.284719Z","submitted_at":"2025-06-26T17:25:49Z","title":"Bridging Offline and Online Reinforcement Learning for LLMs","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T22:28:07.646010Z"},"links":{"cited_paper":"/paper/2401.12086","citing_paper":"/paper/2506.21495"},"observation_digest":"sha256:19aed2fa04141c80d9891f1f324f5c04282cae455b305c4b2a608b6a15834059","observation_id":"d924b7eb-7f25-42fe-836d-3b7a7fe23925","resolution":{"observed_at":"2026-08-06T22:28:07.646010Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12086","last_updated":"2024-10-25T12:04:26Z","snapshot_observed_at":"2026-08-21T19:37:54.327369Z","submitted_at":"2024-01-22T16:24:43Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models","version":2},"cited_work":{"arxiv_id":"2401.12086","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.12086","snapshot_observed_at":"2026-08-05T13:20:39.606465Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models","venue":"cs.CL","work_id":"f32f7260-51f5-4803-bb62-1c69dfb9ce30","year":2024},"citing_paper":{"arxiv_id":"2509.00728","last_updated":"2025-08-31T07:45:40Z","snapshot_observed_at":"2026-08-20T01:49:49.668453Z","submitted_at":"2025-08-31T07:45:40Z","title":"A Survey on Open Dataset Search in the LLM Era: Retrospectives and Perspectives","version":1},"reference_index":115,"source":"pdf_text","source_observed_at":"2026-08-05T13:20:39.171623Z"},"links":{"cited_paper":"/paper/2401.12086","citing_paper":"/paper/2509.00728"},"observation_digest":"sha256:a54b25091387eaf26c43e717e9edfa15ef77c2e49b2d5dd472d8493eb0be393d","observation_id":"b5cc09f4-cc04-4bc2-9819-1bbb87653000","resolution":{"observed_at":"2026-08-05T13:20:39.610566Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12086","last_updated":"2024-10-25T12:04:26Z","snapshot_observed_at":"2026-08-21T19:37:54.327369Z","submitted_at":"2024-01-22T16:24:43Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12086","snapshot_observed_at":"2026-08-16T00:21:59.244898Z","title":"West-of-n: Synthetic preference generation for improved reward modeling","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.12062","last_updated":"2026-08-12T13:48:30Z","snapshot_observed_at":"2026-08-18T16:33:48.146408Z","submitted_at":"2026-08-12T13:48:30Z","title":"Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-16T00:21:59.244898Z"},"links":{"cited_paper":"/paper/2401.12086","citing_paper":"/paper/2608.12062"},"observation_digest":"sha256:07b44cb970344f0e6669a896386a5568a88e14ee3917a44c867d6f5e599e0fc0","observation_id":"f2bc5e41-e5d7-48bc-b5c2-c480fdcea8f8","resolution":{"observed_at":"2026-08-16T00:21:59.244898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2401.12086/citation-record","integrity":"/paper/2401.12086/integrity","json":"/paper/2401.12086/citation-record.json","paper":"/paper/2401.12086"},"outbound":[],"paper":{"arxiv_id":"2401.12086","last_updated":"2024-10-25T12:04:26Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-21T19:37:54.327369Z","submitted_at":"2024-01-22T16:24:43Z","title":"West-of-N: Synthetic Preferences for Self-Improving Reward Models"},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2401.12086."}