{"as_of":"2026-08-07T22:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9d45866dbf9c6f3e7fc3ebe41019bc3f9c1cff7898c102901cea079ada129c86","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":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":14,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:20:00.991880Z","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":"2026-05-21T13:10:10.421442Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-08-07T14:20:00.991880Z","title":null,"venue":null,"work_id":null,"year":1943},"citing_paper":{"arxiv_id":"2505.19428","last_updated":"2025-05-26T02:39:07Z","snapshot_observed_at":"2026-08-07T14:11:57.586207Z","submitted_at":"2025-05-26T02:39:07Z","title":"Frictional Agent Alignment Framework: Slow Down and Don't Break Things","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:20:00.991880Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2505.19428"},"observation_digest":"sha256:022da73254f698a3f67043f18430e1226316fd03d82de8d3cadb411960dd77e6","observation_id":"797bfae3-4e3f-4202-ba83-994136bb68fa","resolution":{"observed_at":"2026-08-07T14:20:00.991880Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-08-07T14:15:19.345978Z","title":"Robust preference optimization through reward model distillation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20359","last_updated":"2025-05-29T13:19:08Z","snapshot_observed_at":"2026-08-07T14:07:21.986904Z","submitted_at":"2025-05-26T08:01:37Z","title":"Risk-aware Direct Preference Optimization under Nested Risk Measure","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:19.345978Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2505.20359"},"observation_digest":"sha256:ce4328eb7a2be3e0811de12aab09e6f9278d0414ee0bba49fd6cad3a251f6930","observation_id":"1acd4098-80ed-4623-bbf1-898a97b2c34f","resolution":{"observed_at":"2026-08-07T14:15:19.345978Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-08-07T14:01:04.861147Z","title":"Robust preference optimization through reward model distillation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.20556","last_updated":"2025-05-26T22:34:42Z","snapshot_observed_at":"2026-08-07T13:49:33.928724Z","submitted_at":"2025-05-26T22:34:42Z","title":"Learning a Pessimistic Reward Model in RLHF","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:01:04.861147Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2505.20556"},"observation_digest":"sha256:4af91706054f8643762d4b96943c42f9954e6869fea214015666e9ca95628429","observation_id":"f499fe4f-e977-411a-88ef-e517db30b68f","resolution":{"observed_at":"2026-08-07T14:01:04.861147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-08-07T12:27:54.536018Z","title":"Robust preference optimization through reward model distillation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24709","last_updated":"2025-05-30T15:30:43Z","snapshot_observed_at":"2026-08-07T12:12:53.183544Z","submitted_at":"2025-05-30T15:30:43Z","title":"On Symmetric Losses for Robust Policy Optimization with Noisy Preferences","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:27:54.536018Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2505.24709"},"observation_digest":"sha256:de33e878a75fb749c6ff3bc4e33ea9e470299904fbe50fd25d28e476e104b901","observation_id":"05043c1f-ced1-415c-9229-4faccb5a5452","resolution":{"observed_at":"2026-08-07T12:27:54.536018Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-08-06T20:55:25.113671Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.01479","last_updated":"2025-07-02T08:43:06Z","snapshot_observed_at":"2026-08-07T09:50:42.126653Z","submitted_at":"2025-07-02T08:43:06Z","title":"Evaluating the Effectiveness of Direct Preference Optimization for Personalizing German Automatic Text Simplifications for Persons with Intellectual Disabilities","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T20:55:25.113671Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2507.01479"},"observation_digest":"sha256:758c873c206763a831ca084bec1ac326661fa4e566bd7ea469c922a60a022b0c","observation_id":"8d036d27-18f1-40b4-9b72-d2a38a1bdf8a","resolution":{"observed_at":"2026-08-06T20:55:25.113671Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-08-06T04:36:13.397543Z","title":"Robust preference optimization through reward model distillation.arXiv preprint arXiv:2405.19316,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.03252","last_updated":"2025-08-27T11:39:11Z","snapshot_observed_at":"2026-08-07T08:32:28.646377Z","submitted_at":"2025-08-05T09:30:39Z","title":"Robust Single-Stage Fully Sparse 3D Object Detection via Detachable Latent Diffusion","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T04:36:13.397543Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2508.03252"},"observation_digest":"sha256:abd8684b5355c41fb929f1de5a008da67a854bdff954a62ad3a919db7a616b04","observation_id":"803e3cde-bdbf-4e6e-849f-6e2179ecd459","resolution":{"observed_at":"2026-08-06T04:36:13.397543Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-08-05T13:24:34.255346Z","title":"Robust preference op- timization through reward model distillation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00685","last_updated":"2025-08-31T03:57:42Z","snapshot_observed_at":"2026-08-07T02:37:09.798161Z","submitted_at":"2025-08-31T03:57:42Z","title":"MPO: Multidimensional Preference Optimization for Language Model-based Text-to-Speech","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T13:24:34.255346Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2509.00685"},"observation_digest":"sha256:0a40c2d09b1f6eacf0d5f1a401fac296d35447476f9e9a3df482309c5db2e663","observation_id":"8134630d-ed0f-48c1-b116-42f1f1a20d78","resolution":{"observed_at":"2026-08-05T13:24:34.255346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":"2405.19316","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Robust preference optimization through reward model distillation.arXiv preprint arXiv:2405.19316","venue":null,"work_id":"58cd9403-b152-4aa6-bfbd-0221d82947a5","year":2025},"citing_paper":{"arxiv_id":"2509.23102","last_updated":"2026-04-06T19:14:12Z","snapshot_observed_at":"2026-08-03T06:33:00.420621Z","submitted_at":"2025-09-27T04:18:33Z","title":"Multiplayer Nash Preference Optimization","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-18T13:09:54.433720Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2509.23102"},"observation_digest":"sha256:ccf3276a9c38455a8369fbc8e69a60febe735c6eaedde433cde7bc8c31e9cba1","observation_id":"76f6a24b-e98e-4788-ac89-2b54f12811da","resolution":{"observed_at":"2026-05-18T13:11:23.953881Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":"2405.19316","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Robust preference optimization through reward model distillation.arXiv preprint arXiv:2405.19316","venue":null,"work_id":"58cd9403-b152-4aa6-bfbd-0221d82947a5","year":2025},"citing_paper":{"arxiv_id":"2512.05929","last_updated":"2026-06-29T18:18:24Z","snapshot_observed_at":"2026-08-03T18:19:06.255338Z","submitted_at":"2025-12-05T18:12:21Z","title":"LLM Harms: A Taxonomy and Discussion","version":2},"reference_index":217,"source":"pdf_text","source_observed_at":"2026-05-17T00:29:07.951709Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2512.05929"},"observation_digest":"sha256:e1669663e814b61e87c5683c85dc907ba97eb445327f4a5110925b816fbe558c","observation_id":"ee0af6d8-2c8e-43f7-9456-befc13bb6f26","resolution":{"observed_at":"2026-05-17T00:31:24.845144Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-08-03T18:19:30.780009Z","title":"Robust Preference Optimization through Reward Model Distillation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.05929","last_updated":"2026-06-29T18:18:24Z","snapshot_observed_at":"2026-08-03T18:19:06.255338Z","submitted_at":"2025-12-05T18:12:21Z","title":"LLM Harms: A Taxonomy and Discussion","version":4},"reference_index":217,"source":"pdf_text","source_observed_at":"2026-08-03T18:19:30.780009Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2512.05929"},"observation_digest":"sha256:ba5e745a5c3b269408b82233bb94f5f6feb7ee2260f54b736970f254a213a09c","observation_id":"d8754ce3-7ed1-4c12-b701-f451f8677142","resolution":{"observed_at":"2026-08-03T18:19:30.780009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":"2405.19316","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Robust preference optimization through reward model distillation.arXiv preprint arXiv:2405.19316","venue":null,"work_id":"58cd9403-b152-4aa6-bfbd-0221d82947a5","year":2025},"citing_paper":{"arxiv_id":"2602.06239","last_updated":"2026-05-15T16:18:46Z","snapshot_observed_at":"2026-07-06T22:44:46.690576Z","submitted_at":"2026-02-05T22:31:07Z","title":"Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-16T06:35:30.479542Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2602.06239"},"observation_digest":"sha256:5c1d38c79417f6d108f41caf8c00d0cf3222ede4fb26eb5eba3637709496466f","observation_id":"7e62ac6f-5cce-443d-a546-a63ae0fd0e8c","resolution":{"observed_at":"2026-05-16T06:37:28.585241Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":"2405.19316","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Robust preference optimization through reward model distillation.arXiv preprint arXiv:2405.19316","venue":null,"work_id":"58cd9403-b152-4aa6-bfbd-0221d82947a5","year":2025},"citing_paper":{"arxiv_id":"2602.06239","last_updated":"2026-05-15T16:18:46Z","snapshot_observed_at":"2026-07-06T22:44:46.690576Z","submitted_at":"2026-02-05T22:31:07Z","title":"Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-21T13:06:54.002248Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2602.06239"},"observation_digest":"sha256:fe5bc21ae0a9a492a069dd02437d931336e7910b8bdc264815a3d278a7260a7b","observation_id":"76156812-159e-4015-bb05-c44fe90c4f7e","resolution":{"observed_at":"2026-05-21T13:10:10.423914Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":"2405.19316","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Robust preference optimization through reward model distillation.arXiv preprint arXiv:2405.19316","venue":null,"work_id":"58cd9403-b152-4aa6-bfbd-0221d82947a5","year":2025},"citing_paper":{"arxiv_id":"2604.24536","last_updated":"2026-04-27T14:33:45Z","snapshot_observed_at":"2026-07-06T23:10:33.821313Z","submitted_at":"2026-04-27T14:33:45Z","title":"Generating Place-Based Compromises Between Two Points of View","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-08T03:36:31.695964Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2604.24536"},"observation_digest":"sha256:103c34d7af29fd505674f1e631c5a50b95a8d1071b89c86d5858afd7f808ce64","observation_id":"8da4c509-4e9c-464b-b81b-2995af2b488f","resolution":{"observed_at":"2026-05-11T22:01:12.122390Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19316","snapshot_observed_at":"2026-08-02T10:01:57.377453Z","title":"Robust preference optimization through reward model distillation.arXiv preprint arXiv:2405.19316,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16240","last_updated":"2026-06-26T03:03:15Z","snapshot_observed_at":"2026-08-04T11:34:03.080529Z","submitted_at":"2026-06-26T03:03:15Z","title":"Normalized Rewards for Preference Optimization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T10:01:57.377453Z"},"links":{"cited_paper":"/paper/2405.19316","citing_paper":"/paper/2607.16240"},"observation_digest":"sha256:fa7ee8277f825292d699ec0cc238301ea9d3fdaa99a1dc761c38be2bcef1d2fa","observation_id":"52382d59-bd63-41ce-87da-ffbec13e15e0","resolution":{"observed_at":"2026-08-02T10:01:57.377453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2405.19316/citation-record","integrity":"/paper/2405.19316/integrity","json":"/paper/2405.19316/citation-record.json","paper":"/paper/2405.19316"},"outbound":[],"paper":{"arxiv_id":"2405.19316","last_updated":"2025-03-03T08:22:25Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-02T12:00:09.704648Z","submitted_at":"2024-05-29T17:39:48Z","title":"Robust Preference Optimization through Reward Model Distillation"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2405.19316."}