{"as_of":"2026-08-16T00:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2e0e20a7cd10a68c9bb2e68a8aad09acbeba6b5dbb1fd98b185d5df4010df4a0","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:20:35.039153Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"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":[],"links":{"evidence":"/evidence","html":"/paper/2506.18670/citation-record","integrity":"/paper/2506.18670/integrity","json":"/paper/2506.18670/citation-record.json","paper":"/paper/2506.18670"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:20:37.599529Z","title":"Umass at trec 2004: Novelty and hard","venue":null,"work_id":"74d02112-6c8e-4c78-9f2f-266a350312b1","year":2004},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:32.605177Z"},"links":{"citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:4c9320085695fccb2286fa4ff78baee83661202796bed43ecc6effef2492d572","observation_id":"79fa8d19-cdb3-4c89-9a72-22901181c182","resolution":{"observed_at":"2026-08-06T23:20:37.691985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:20:37.423259Z","title":"The snowflake elastic data warehouse","venue":null,"work_id":"1e8df78e-401d-4d2a-8890-5c23ebb85871","year":2016},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:32.719082Z"},"links":{"citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:75912a9ac634d16811b2e729097fba2ce08f18a91332fa0976b87c40e02224d4","observation_id":"4e1cdd66-c5b3-4ea9-b701-efbbb5f65af7","resolution":{"observed_at":"2026-08-06T23:20:37.524777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:20:37.231316Z","title":"Dense passage retrieval for open-domain question answering","venue":null,"work_id":"8cf63423-848f-4fe6-9b50-f6dc3bfd0d6f","year":2020},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.240921Z"},"links":{"citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:75af4884e6e7d21b20b89bff248a05df8b84848d4e521191ae88547af332a014","observation_id":"20186bc2-508f-4606-b4c1-13dd498c8fd2","resolution":{"observed_at":"2026-08-06T23:20:37.325696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.14024","last_updated":"2023-01-23T17:00:01Z","snapshot_observed_at":"2026-08-14T16:33:48.199064Z","submitted_at":"2022-12-28T18:52:44Z","title":"Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.14024","snapshot_observed_at":"2026-08-06T23:20:33.318382Z","title":"Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.318382Z"},"links":{"cited_paper":"/paper/2212.14024","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:19859269e4da983effb10ba78e260fcf84a3bfc5dfffdeeec637e8ad0bc9bc51","observation_id":"b23ba221-dd4a-4a6a-8638-3338fc4540eb","resolution":{"observed_at":"2026-08-06T23:20:33.318382Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.05115","last_updated":"2022-05-23T16:41:08Z","snapshot_observed_at":"2026-08-14T10:24:18.417908Z","submitted_at":"2022-03-10T02:24:14Z","title":"Internet-augmented language models through few-shot prompting for open-domain question answering","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.05115","snapshot_observed_at":"2026-08-06T23:20:33.395421Z","title":"Internet- augmented language models through few-shot prompting for open-domain question answering","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.395421Z"},"links":{"cited_paper":"/paper/2203.05115","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:4be1fc768b7db852434c9e42cbd795422bbd01dea9c7e761b27d6fc1fceff796","observation_id":"7538546d-82ba-4388-8348-3d50c4e685e2","resolution":{"observed_at":"2026-08-06T23:20:33.395421Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:20:36.715421Z","title":"Query rewriting in retrieval- augmented large language models","venue":null,"work_id":"7bab6e98-5b88-4696-9ee4-60ce528160b3","year":2023},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.504793Z"},"links":{"citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:f2601173a5666969f0f0f0eacdf6bd7701fc7130e5563cabbebe43389641695c","observation_id":"e65e2d00-a61d-4f3e-833f-10908d9bc588","resolution":{"observed_at":"2026-08-06T23:20:36.834810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14431","last_updated":"2024-05-23T11:00:19Z","snapshot_observed_at":"2026-08-12T23:59:59.676859Z","submitted_at":"2024-05-23T11:00:19Z","title":"RaFe: Ranking Feedback Improves Query Rewriting for RAG","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14431","snapshot_observed_at":"2026-08-06T23:20:33.552216Z","title":"Rafe: ranking feedback improves query rewriting for rag.arXiv preprint arXiv:2405.14431,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.552216Z"},"links":{"cited_paper":"/paper/2405.14431","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:c99a461e24913268685bcc7fc1396c06ba02ab13ce3c3a0cb3bcad78f05280dc","observation_id":"05151d47-7f33-407c-abc0-81e1de0a52c3","resolution":{"observed_at":"2026-08-06T23:20:33.552216Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03350","last_updated":"2023-10-17T18:57:17Z","snapshot_observed_at":"2026-08-15T17:25:43.175408Z","submitted_at":"2022-10-07T06:50:23Z","title":"Measuring and Narrowing the Compositionality Gap in Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03350","snapshot_observed_at":"2026-08-06T23:20:33.669571Z","title":"Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.669571Z"},"links":{"cited_paper":"/paper/2210.03350","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:0a0d108569a4b0cd10ec20179008e74dff0b4577adccd8f084b422ad20303d9f","observation_id":"87fe10bd-61ec-4bea-a7b7-0679ec8b0b70","resolution":{"observed_at":"2026-08-06T23:20:33.669571Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.14233","last_updated":"2023-08-02T02:06:28Z","snapshot_observed_at":"2026-08-13T11:53:58.611322Z","submitted_at":"2023-04-27T14:45:55Z","title":"Large Language Models are Strong Zero-Shot Retriever","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.14233","snapshot_observed_at":"2026-08-06T23:20:33.901661Z","title":"Large language models are strong zero-shot retriever.arXiv preprint arXiv:2304.14233,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.901661Z"},"links":{"cited_paper":"/paper/2304.14233","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:6fc52782a8b9b573e1429cae22bc22874ce5de41eccc6697f6a70e895fc82608","observation_id":"0d73d76e-2742-4d23-967f-7d90ae614c00","resolution":{"observed_at":"2026-08-06T23:20:33.901661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.19256","last_updated":"2024-10-02T04:01:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-28T06:20:03Z","title":"HybridFlow: A Flexible and Efficient RLHF Framework","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.19256","snapshot_observed_at":"2026-08-06T23:20:33.950971Z","title":"Hybridflow: A flexible and efficient rlhf framework.arXiv preprint arXiv: 2409.19256,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.950971Z"},"links":{"cited_paper":"/paper/2409.19256","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:79ebdc2fb9940f7e83f936d27c45132a93692c8e8831d597726107d5cb31b0b1","observation_id":"4d5aacc5-daf4-4d1a-8017-b0d10ac2db09","resolution":{"observed_at":"2026-08-06T23:20:33.950971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.12652","last_updated":"2023-05-24T05:08:07Z","snapshot_observed_at":"2026-08-02T02:08:12.414817Z","submitted_at":"2023-01-30T04:18:09Z","title":"REPLUG: Retrieval-Augmented Black-Box Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.12652","snapshot_observed_at":"2026-08-06T23:20:34.022956Z","title":"Replug: Retrieval-augmented black-box language models.arXiv preprint arXiv:2301.12652,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:34.022956Z"},"links":{"cited_paper":"/paper/2301.12652","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:bbb649b75c267fc1eb9e038d36410bdb8f7cc357ed81cc3c34396cb9334cc8ea","observation_id":"ab97999b-f31d-4919-a89e-6b1423ec8dcc","resolution":{"observed_at":"2026-08-06T23:20:34.022956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.07678","last_updated":"2023-10-11T08:34:42Z","snapshot_observed_at":"2026-08-14T20:44:12.019145Z","submitted_at":"2023-03-14T07:27:30Z","title":"Query2doc: Query Expansion with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.07678","snapshot_observed_at":"2026-08-06T23:20:34.396029Z","title":"Query2doc: Query expansion with large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:34.396029Z"},"links":{"cited_paper":"/paper/2303.07678","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:1ebe6c561db6419b639aae449648afea0ca2c7019c4131b1d28a7d4c86dd36c1","observation_id":"972397bb-0e2d-4add-b463-bb80b7c22ca2","resolution":{"observed_at":"2026-08-06T23:20:34.396029Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05672","last_updated":"2024-02-08T13:47:50Z","snapshot_observed_at":"2026-08-12T15:58:37.148545Z","submitted_at":"2024-02-08T13:47:50Z","title":"Multilingual E5 Text Embeddings: A Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.05672","snapshot_observed_at":"2026-08-06T23:20:34.478229Z","title":"Multilin- gual e5 text embeddings: A technical report.arXiv preprint arXiv:2402.05672,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:34.478229Z"},"links":{"cited_paper":"/paper/2402.05672","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:e368c4b8d1ba857e17baf2e92a8496434f0aa971135bd073fc16fcfd7ab4544d","observation_id":"4b4b9822-f99f-4585-801e-c208140af0ec","resolution":{"observed_at":"2026-08-06T23:20:34.478229Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:20:36.174774Z","title":"Query expansion with freebase.Proceedings of the 2015 International Conference on The Theory of Information Retrieval,","venue":null,"work_id":"c4ce2833-76ee-4758-a1de-e69844f20a92","year":2015},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:34.616862Z"},"links":{"citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:d99f09d1f8477e49b45352708c4eb8ce434f509bce2ee010c7cc1b892c129d75","observation_id":"8acf5575-aed1-41f6-b98e-495124b08d58","resolution":{"observed_at":"2026-08-06T23:20:36.290421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.09716","last_updated":"2023-10-18T13:48:03Z","snapshot_observed_at":"2026-08-13T05:49:13.343936Z","submitted_at":"2023-10-15T03:04:17Z","title":"Enhancing Conversational Search: Large Language Model-Aided Informative Query Rewriting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.09716","snapshot_observed_at":"2026-08-06T23:20:34.734211Z","title":"Enhancing conversational search: Large language model-aided informative query rewriting.arXiv preprint arXiv:2310.09716,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:34.734211Z"},"links":{"cited_paper":"/paper/2310.09716","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:27136b3aa47c267e2843ae3609def86c3185480d756976a6eb00aab331424b03","observation_id":"55e68d54-7f8a-4167-bb2e-bed4084c9bad","resolution":{"observed_at":"2026-08-06T23:20:34.734211Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:20:35.767833Z","title":"The best results are bolded, and other top-three results are underlined","venue":null,"work_id":"6551a17e-2975-4fa4-9267-a7f1c3a0329e","year":2018},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:34.925176Z"},"links":{"citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:5e8e5620202f8a1857e3bb662a6816ff2604f922e95e088d67a7729449f15541","observation_id":"85f834c6-fc70-45e6-8e58-6505313c2126","resolution":{"observed_at":"2026-08-06T23:20:35.862890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:20:35.489201Z","title":null,"venue":null,"work_id":"d6eb561a-cc3a-46b3-b82b-269fa29507a9","year":2025},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:35.039153Z"},"links":{"citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:4c9829ec25029316f189ee332f814adf0b418cceb31bd356eacb6558cf9e8b20","observation_id":"ac510c28-a231-4374-afb1-860b06c7d40e","resolution":{"observed_at":"2026-08-06T23:20:35.572703Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:20:32.637146Z","title":"Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":1982,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:32.637146Z"},"links":{"citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:65122cd7f73ae9710690890a60bf0002c7600824b4dac6c9891de25b20c1beb2","observation_id":"b94e965a-0b18-4840-a46d-89f518f9e607","resolution":{"observed_at":"2026-08-06T23:20:32.637146Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-15T20:26:32.102285Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-06T23:20:33.836645Z","title":"Proximal policy optimization algorithms.ArXiv, abs/1707.06347,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":1988,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.836645Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:ddd13de1feca6e6ef05f57087eb2fcc663bd57f11810e93def74ff1b4ba850ce","observation_id":"6a151714-9eed-4091-a757-208583949cb7","resolution":{"observed_at":"2026-08-06T23:20:33.836645Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.01325","last_updated":"2022-02-15T19:09:36Z","snapshot_observed_at":"2026-08-12T00:02:16.697336Z","submitted_at":"2020-09-02T19:54:41Z","title":"Learning to summarize from human feedback","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.01325","snapshot_observed_at":"2026-08-06T23:20:34.126560Z","title":"Ziegler, Ryan J","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2001,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:34.126560Z"},"links":{"cited_paper":"/paper/2009.01325","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:52ae4a8334ae49fd19e2e273373507616935bc1192f0025f55bea0aee4530dc1","observation_id":"485abd80-0eaa-4066-918c-5f70f1482e8f","resolution":{"observed_at":"2026-08-06T23:20:34.126560Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.14924","last_updated":"2024-09-23T11:20:20Z","snapshot_observed_at":"2026-08-13T05:59:29.635188Z","submitted_at":"2024-09-23T11:20:20Z","title":"Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.14924","snapshot_observed_at":"2026-08-06T23:20:34.827663Z","title":"Siyun Zhao, Yuqing Yang, Zilong Wang, Zhiyuan He, Luna K","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2008,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:34.827663Z"},"links":{"cited_paper":"/paper/2409.14924","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:4024622f54ad49d32bf7324e39e9e3182f20888028b8a0971561d1184187db5c","observation_id":"48056f53-88b4-4252-9b74-57141a8c9a6a","resolution":{"observed_at":"2026-08-06T23:20:34.827663Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:20:36.460026Z","title":"Hamilton, Chris Dyer, and Dani Yogatama","venue":null,"work_id":"0bb920b8-8382-4ae5-86ee-d6df115ce494","year":2021},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2009,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.770375Z"},"links":{"citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:d80d43508b3554bdb721685a0a89587311b5e353e49c17f01b9537420231656d","observation_id":"9a58daef-afd3-4083-806f-55bfefd012a9","resolution":{"observed_at":"2026-08-06T23:20:36.544748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08663","last_updated":"2021-10-21T01:18:28Z","snapshot_observed_at":"2026-08-09T23:22:21.200279Z","submitted_at":"2021-04-17T23:29:55Z","title":"BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08663","snapshot_observed_at":"2026-08-06T23:20:34.235230Z","title":"Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models.arXiv preprint arXiv:2104.08663,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2010,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:34.235230Z"},"links":{"cited_paper":"/paper/2104.08663","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:7810baebc66fdc9a419c109044b529dfee385e16440519f80bdbfdb4c393a3cf","observation_id":"018011c7-66aa-410b-97a7-4c2ebbe1bb2c","resolution":{"observed_at":"2026-08-06T23:20:34.235230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.08281","last_updated":"2025-10-23T09:36:08Z","snapshot_observed_at":"2026-07-31T05:45:37.385210Z","submitted_at":"2024-01-16T11:12:36Z","title":"The Faiss library","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.08281","snapshot_observed_at":"2026-08-06T23:20:32.777818Z","title":"The faiss library.arXiv preprint arXiv:2401.08281,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:32.777818Z"},"links":{"cited_paper":"/paper/2401.08281","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:735b066a9eb79d188223fcdafc3e898a91d76d51076ec375a3ceefbca8c2ed23","observation_id":"dec8fa1e-815e-4e34-af85-c3f40b074e0d","resolution":{"observed_at":"2026-08-06T23:20:32.777818Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03741","last_updated":"2023-02-17T17:00:34Z","snapshot_observed_at":"2026-08-12T12:29:44.507445Z","submitted_at":"2017-06-12T17:23:59Z","title":"Deep reinforcement learning from human preferences","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03741","snapshot_observed_at":"2026-08-06T23:20:32.682670Z","title":"Brown, Miljan Martic, Shane Legg, and Dario Amodei","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:32.682670Z"},"links":{"cited_paper":"/paper/1706.03741","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:e69fe59e5570d7678b5e204f2e05598ad9f82cbacbdfc12f70942109e99bb906","observation_id":"af19faba-db54-47d0-b29d-51a38a178c0c","resolution":{"observed_at":"2026-08-06T23:20:32.682670Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.02155","last_updated":"2022-03-04T07:04:42Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-03-04T07:04:42Z","title":"Training language models to follow instructions with human feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.02155","snapshot_observed_at":"2026-08-06T23:20:33.608260Z","title":"Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke E","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.608260Z"},"links":{"cited_paper":"/paper/2203.02155","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:7dcfe5076ec6f1e53809ad97878f0e94bf7ea98460e939a508706bc438e6a732","observation_id":"4310ec17-a2b9-458f-8c47-416bd189990a","resolution":{"observed_at":"2026-08-06T23:20:33.608260Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T23:20:36.936379Z","title":"You only need one model for open-domain question answering","venue":null,"work_id":"69976ced-7169-4880-b678-56f487381fb1","year":2022},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.444259Z"},"links":{"citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:74b348c843617637c889db8cef3fad08993fd2f19249d917b09ff237b362bcfe","observation_id":"5f5d099a-4029-4665-a511-63766b0cc725","resolution":{"observed_at":"2026-08-06T23:20:37.075468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.23214","last_updated":"2024-10-31T01:34:16Z","snapshot_observed_at":"2026-08-12T22:11:35.394981Z","submitted_at":"2024-10-30T17:02:54Z","title":"Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.23214","snapshot_observed_at":"2026-08-06T23:20:32.987960Z","title":"Grounding by trying: Llms with reinforcement learning-enhanced retrieval.arXiv preprint arXiv:2410.23214,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:32.987960Z"},"links":{"cited_paper":"/paper/2410.23214","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:bb73dcc3b198bda8e55f90b6a0d82568b9e8b5f6a28f0578da17f8ec9b07dc55","observation_id":"5487c94a-887e-4904-a508-d404b5b6fd49","resolution":{"observed_at":"2026-08-06T23:20:32.987960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-06T23:20:34.331047Z","title":"Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:34.331047Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:886348646e2cb9913ad6802505f113afaeb933f8cb9625924ad4a882a4aa540c","observation_id":"c2d4a71f-ae04-4fdb-a072-61723c9d16ef","resolution":{"observed_at":"2026-08-06T23:20:34.331047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-13T19:45:21.517834Z","submitted_at":"2025-02-28T22:16:42Z","title":"DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-06T23:20:33.163275Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning.arXiv preprint arXiv:2503.00223,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.163275Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:f28f5cd4a7f192331f4e7972963f0224626500ac290e5f9c37866f75a88537dd","observation_id":"74867ca8-411d-4ef4-a9c4-a300bbb92cb6","resolution":{"observed_at":"2026-08-06T23:20:33.163275Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-15T12:33:55.451951Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-06T23:20:32.914472Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.arXiv preprint arXiv:2501.12948,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:32.914472Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:ce1bdfb24441d175c2536a8120e73c71a66befdd5a4645dedcbfcd5d2674815f","observation_id":"a7b57add-abc9-42b3-b093-f1a095e911ca","resolution":{"observed_at":"2026-08-06T23:20:32.914472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10997","last_updated":"2024-03-27T09:16:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-18T07:47:33Z","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.10997","snapshot_observed_at":"2026-08-06T23:20:32.857634Z","title":"Retrieval-augmented generation for large language models: A survey.arXiv preprint arXiv:2312.10997, 2:1,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:32.857634Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:3addb3cf1d4cc24498555ef7055e86f53d117f5563cc4c7756c4e20363ebd8e9","observation_id":"8b3849b0-f77e-42db-ad16-6dc09ccd52a0","resolution":{"observed_at":"2026-08-06T23:20:32.857634Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.03262","last_updated":"2025-11-10T15:11:13Z","snapshot_observed_at":"2026-08-02T05:27:47.490711Z","submitted_at":"2025-01-04T02:08:06Z","title":"REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.03262","snapshot_observed_at":"2026-08-06T23:20:33.108723Z","title":"Reinforce++: A simple and efficient approach for aligning large language models.arXiv preprint arXiv:2501.03262,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T23:20:33.108723Z"},"links":{"cited_paper":"/paper/2501.03262","citing_paper":"/paper/2506.18670"},"observation_digest":"sha256:866ec9c11388a20cfceab99fce182a5d4c40138d12075ef5793bb43cb86b3172","observation_id":"b8150b4c-7826-4b4e-b19d-35af7d4f7ec9","resolution":{"observed_at":"2026-08-06T23:20:33.108723Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.18670","last_updated":"2025-06-23T14:14:43Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-14T05:38:16.135669Z","submitted_at":"2025-06-23T14:14:43Z","title":"Harnessing the Power of Reinforcement Learning for Language-Model-Based Information Retriever via Query-Document Co-Augmentation"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":0,"verified_fuzzy":8},"total_outbound_references":33},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2506.18670."}