{"as_of":"2026-08-05T22:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:528fc7f2077fbe5c6b54335e5aa47938dc96c3a446d494af9af689becbf11afa","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":16,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":16,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":16,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":16,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T05:26:01.675314Z","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":"2026-07-10T06:15:00.866473Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2412.04300","last_updated":"2025-06-04T03:29:18Z","snapshot_observed_at":"2026-08-02T07:59:46.765003Z","submitted_at":"2024-12-05T16:21:01Z","title":"T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts","version":3},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-05-23T08:00:12.781392Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2412.04300"},"observation_digest":"sha256:a291a1f6549d32b12990971a3115e7a41985a7df60e1ea628d9d939e7c801307","observation_id":"519dfa99-5557-425f-8788-51f51b6b75ce","resolution":{"observed_at":"2026-06-10T02:11:10.714292Z","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2507.05179","last_updated":"2026-05-31T04:19:46Z","snapshot_observed_at":"2026-08-04T10:31:41.400604Z","submitted_at":"2025-07-07T16:34:28Z","title":"From Fragments to Facts: A Curriculum-Driven DPO Approach for Generating Hindi News Veracity Explanations","version":5},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-19T06:09:26.269452Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2507.05179"},"observation_digest":"sha256:39371d279824c5ad4adaf30338181529630f2f7e9a3b6c83ac837cd1f6127d6f","observation_id":"9e4f5b25-ea62-4f09-bbf0-0c4acd5eda14","resolution":{"observed_at":"2026-06-10T02:11:10.714292Z","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2509.02547","last_updated":"2026-04-17T18:09:08Z","snapshot_observed_at":"2026-08-03T09:07:42.489237Z","submitted_at":"2025-09-02T17:46:26Z","title":"The Landscape of Agentic Reinforcement Learning for LLMs: A Survey","version":5},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-18T19:19:36.427337Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2509.02547"},"observation_digest":"sha256:49d2e0017f1fb387965abad1c68a4b7c41ee577184892c660f54cceef468bbc4","observation_id":"53a7ab6b-16a0-4246-bd5b-83e320dda34c","resolution":{"observed_at":"2026-06-10T02:11:10.714292Z","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-08-03T05:26:01.675314Z","title":"A comprehensive sur- vey of direct preference optimization.arXiv preprint arXiv:2410.15595,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.02451","last_updated":"2026-06-18T18:51:33Z","snapshot_observed_at":"2026-08-03T05:26:00.081594Z","submitted_at":"2026-02-02T18:43:52Z","title":"Active Causal Experimentalist (ACE): Learning Intervention Strategies via Direct Preference Optimization","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-03T05:26:01.675314Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2602.02451"},"observation_digest":"sha256:08975b40d7c1cd11405d43aa6fe29c4d00fb23d03c068c54f99b83e91ae8fcab","observation_id":"98c858db-038a-4c6e-9a94-6d9fc951bdf2","resolution":{"observed_at":"2026-08-03T05:26:01.675314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2604.27859","last_updated":"2026-05-15T06:25:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-30T13:43:25Z","title":"Rethinking Agentic Reinforcement Learning In Large Language Models","version":1},"reference_index":106,"source":"pdf_text","source_observed_at":"2026-05-07T06:30:09.945371Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2604.27859"},"observation_digest":"sha256:337048076241b9daed110161b72e782928ca6f10ed83c64e39fe23b9a6a54ee3","observation_id":"3013383f-6f4a-4153-95b5-e41680157898","resolution":{"observed_at":"2026-06-10T02:11:10.714292Z","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2604.27859","last_updated":"2026-05-15T06:25:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-30T13:43:25Z","title":"Rethinking Agentic Reinforcement Learning In Large Language Models","version":2},"reference_index":106,"source":"pdf_text","source_observed_at":"2026-05-08T03:12:19.414358Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2604.27859"},"observation_digest":"sha256:a0b60540584d200bcbad90ad68326fb7f4a832340492a3c22a4573739875e385","observation_id":"72b588ce-8bf1-44d0-8e97-596c09337a06","resolution":{"observed_at":"2026-06-10T02:11:10.714292Z","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2604.27859","last_updated":"2026-05-15T06:25:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-30T13:43:25Z","title":"Rethinking Agentic Reinforcement Learning In Large Language Models","version":3},"reference_index":106,"source":"pdf_text","source_observed_at":"2026-05-19T16:58:41.558250Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2604.27859"},"observation_digest":"sha256:0403bf84f07f212feafa810b514fb3548e745d8baf57afd4e5d767c9269fb37a","observation_id":"942de73f-1e2a-400a-883e-7b98baa62f04","resolution":{"observed_at":"2026-06-10T02:11:10.714292Z","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2605.11679","last_updated":"2026-05-13T09:28:34Z","snapshot_observed_at":"2026-07-06T23:23:30.499023Z","submitted_at":"2026-05-12T07:38:59Z","title":"Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansion","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-13T01:03:10.263663Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2605.11679"},"observation_digest":"sha256:b7aa296d6b82ad2238151e339b65cc7c91346739f921fd2308d04e652a9302d0","observation_id":"02f081b9-9386-4801-9343-faad5808d4b8","resolution":{"observed_at":"2026-06-10T02:11:10.714292Z","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2605.11679","last_updated":"2026-05-13T09:28:34Z","snapshot_observed_at":"2026-07-06T23:23:30.499023Z","submitted_at":"2026-05-12T07:38:59Z","title":"Explaining and Breaking the Safety-Helpfulness Ceiling via Preference Dimensional Expansion","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-14T21:12:06.989077Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2605.11679"},"observation_digest":"sha256:4a0d798e44f66767995aced2c06867a930c7c65f7926b4af22d73563d82e6f83","observation_id":"5d75473b-9ac6-4a3d-b4b1-9e20570849a5","resolution":{"observed_at":"2026-06-10T02:11:10.714292Z","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2605.12288","last_updated":"2026-06-10T07:32:21Z","snapshot_observed_at":"2026-07-06T23:23:59.123377Z","submitted_at":"2026-05-12T15:44:33Z","title":"TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching","version":1},"reference_index":189,"source":"arxiv_source","source_observed_at":"2026-05-13T04:55:55.013900Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2605.12288"},"observation_digest":"sha256:ab85e1fa1be5621ceea1bde7c6a3d633cbd8f60cc9677affbf3ffe017e70994e","observation_id":"bbb00dc8-427e-4690-98d6-7d2477d6ca53","resolution":{"observed_at":"2026-06-10T02:11:10.714292Z","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2605.12288","last_updated":"2026-06-10T07:32:21Z","snapshot_observed_at":"2026-07-06T23:23:59.123377Z","submitted_at":"2026-05-12T15:44:33Z","title":"TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-15T05:41:10.714594Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2605.12288"},"observation_digest":"sha256:2ac53085224df16514b835001ed102ce222f3014d4418bac1316d7cc9bbd7abc","observation_id":"88539dbc-8626-4e97-ba06-9cded6ea9dac","resolution":{"observed_at":"2026-06-10T02:11:10.714292Z","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2605.12288","last_updated":"2026-06-10T07:32:21Z","snapshot_observed_at":"2026-07-06T23:23:59.123377Z","submitted_at":"2026-05-12T15:44:33Z","title":"TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-11T11:50:26.030339Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2605.12288"},"observation_digest":"sha256:1c69fc6c2631d369ef8119fb9bf2a29171f0d1ba999d7e6555f2df9001848154","observation_id":"d2a656bc-9798-4349-a83e-5b4be92d3abf","resolution":{"observed_at":"2026-06-30T22:25:06.439296Z","resolver_source":"local_arxiv","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2605.12288","last_updated":"2026-06-10T07:32:21Z","snapshot_observed_at":"2026-07-06T23:23:59.123377Z","submitted_at":"2026-05-12T15:44:33Z","title":"TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-30T22:15:53.639048Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2605.12288"},"observation_digest":"sha256:82d957eecc25430629736f9244f0e5319196965319d6be27fbc3c81521ca801a","observation_id":"ab5e5094-07ec-4c79-9063-00f45ac2f4b2","resolution":{"observed_at":"2026-06-30T22:25:06.431552Z","resolver_source":"local_arxiv","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2606.18323","last_updated":"2026-06-16T15:41:44Z","snapshot_observed_at":"2026-07-06T23:53:45.117607Z","submitted_at":"2026-06-16T15:41:44Z","title":"Reliable Neural-Codec Text-to-Speech by ASR Self-Verification and Distillation: Near-Zero Catastrophic Failures Across Models and Codecs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T22:46:47.786929Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2606.18323"},"observation_digest":"sha256:1fcaf2511ab3a1af123fcef3f422d905fe8f1ce2f6b089cf087237662c65a68b","observation_id":"97fe8aa6-09c7-463d-80d0-c2b16d2dc714","resolution":{"observed_at":"2026-07-03T23:19:02.875664Z","resolver_source":"local_arxiv","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":"2410.15595","doi":"10.48550/arxiv.2410.15595","metadata_source":"pith","pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A comprehensive survey of datasets, theories, variants, and applications in direct preference optimization","venue":"cs.AI","work_id":"fbcda931-44f5-4714-9437-c62403696afb","year":2024},"citing_paper":{"arxiv_id":"2606.21943","last_updated":"2026-06-20T08:20:41Z","snapshot_observed_at":"2026-07-06T23:56:54.959593Z","submitted_at":"2026-06-20T08:20:41Z","title":"Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning","version":1},"reference_index":233,"source":"pdf_text","source_observed_at":"2026-06-26T12:15:08.304150Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2606.21943"},"observation_digest":"sha256:822aeeb02b2ee79e1d933fe095b76ff19a5baec24f7936cc4d34eb72d1b40592","observation_id":"e86b1d7e-f9db-4e1d-b288-cefa5e6e7f8a","resolution":{"observed_at":"2026-07-04T08:09:40.699362Z","resolver_source":"local_arxiv","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":"2410.15595","last_updated":"2026-06-09T05:48:30Z","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.15595","snapshot_observed_at":"2026-08-01T10:59:18.707247Z","title":"A comprehensive survey of direct preference optimization: Datasets, theories, variants, and applications.arXiv preprint arXiv:2410.15595, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.20057","last_updated":"2026-07-22T11:59:38Z","snapshot_observed_at":"2026-08-05T01:59:04.033220Z","submitted_at":"2026-07-22T11:59:38Z","title":"Antigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-01T10:59:18.707247Z"},"links":{"cited_paper":"/paper/2410.15595","citing_paper":"/paper/2607.20057"},"observation_digest":"sha256:c043747bc8cb08e28c171c66795697ca9d4aa259b5497caff32e195c5e3f2d91","observation_id":"65e0b408-5a26-4e3f-ae87-b9879ef5963c","resolution":{"observed_at":"2026-08-01T10:59:18.707247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2410.15595/citation-record","integrity":"/paper/2410.15595/integrity","json":"/paper/2410.15595/citation-record.json","paper":"/paper/2410.15595"},"outbound":[],"paper":{"arxiv_id":"2410.15595","last_updated":"2026-06-09T05:48:30Z","latest_version":4,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-06T19:36:45.701678Z","submitted_at":"2024-10-21T02:27:24Z","title":"A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications"},"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 16 inbound Pith citation observations for arXiv:2410.15595."}