{"as_of":"2026-08-18T04:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4b22b6dc1595a708608d8985203edb1b16842f333bb96da2c33a8d8af6c8dd0a","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":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":37,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T12:02:43.163565Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2502.13957","last_updated":"2026-05-16T17:37:22Z","snapshot_observed_at":"2026-08-16T06:21:33.550307Z","submitted_at":"2025-02-19T18:56:03Z","title":"Supervising the search process produces reliable and generalizable information-seeking agents","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-23T02:18:27.204122Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2502.13957"},"observation_digest":"sha256:bb8d406e88fef45349981e307ee47bffa0c5af835623ea56042ec6e65f291aa7","observation_id":"92e40c64-c24d-435d-9768-d73dcd9df392","resolution":{"observed_at":"2026-05-23T02:22:25.425822Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2504.01990","last_updated":"2025-08-02T12:44:02Z","snapshot_observed_at":"2026-08-12T16:45:40.094278Z","submitted_at":"2025-03-31T18:00:29Z","title":"Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems","version":2},"reference_index":152,"source":"pdf_text","source_observed_at":"2026-05-22T21:39:49.832151Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2504.01990"},"observation_digest":"sha256:1faf950a71ad874f81ea78b8ffba017c2410d637cbf98a3f478cc1c0a8ac576c","observation_id":"63e574af-d708-4df1-9c00-db0f9766a112","resolution":{"observed_at":"2026-05-22T21:42:10.633193Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2026-08-16T12:02:43.163565Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.13828","last_updated":"2025-04-28T12:41:07Z","snapshot_observed_at":"2026-08-18T03:47:57.203659Z","submitted_at":"2025-04-18T17:55:58Z","title":"Generative AI Act II: Test Time Scaling Drives Cognition Engineering","version":3},"reference_index":140,"source":"arxiv_source","source_observed_at":"2026-08-16T12:02:43.163565Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2504.13828"},"observation_digest":"sha256:e5692205e0bf37cb1eb71745d73e5ccdd1f3afc914e46d2bd2d89823160c0860","observation_id":"d51d3fbd-0a7f-4f24-8903-5d0eb1e50c3c","resolution":{"observed_at":"2026-08-16T12:02:43.163565Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-16T11:18:44.499446Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.15909","last_updated":"2025-04-24T12:39:35Z","snapshot_observed_at":"2026-08-17T16:59:38.075315Z","submitted_at":"2025-04-22T13:55:13Z","title":"Synergizing RAG and Reasoning: A Systematic Review","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-16T11:18:44.499446Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2504.15909"},"observation_digest":"sha256:42497b8bafe5ed023b47b3444e7542f36493a604a234fb1d4a4aed9a65eb105c","observation_id":"1e834f5e-c856-4e03-bca5-c7395ddd959e","resolution":{"observed_at":"2026-08-16T11:18:44.499446Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-07T13:24:50.943846Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.22019","last_updated":"2025-06-03T05:28:55Z","snapshot_observed_at":"2026-08-17T09:42:33.328787Z","submitted_at":"2025-05-28T06:30:51Z","title":"VRAG-RL: Empower Vision-Perception-Based RAG for Visually Rich Information Understanding via Iterative Reasoning with Reinforcement Learning","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T13:24:50.943846Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2505.22019"},"observation_digest":"sha256:3f71994e831fec4fb9a1540eb918393834620df8e763eb20feed42992d85eb59","observation_id":"f7548be0-6977-4d9b-8ed3-e540525ea30f","resolution":{"observed_at":"2026-08-07T13:24:50.943846Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-07T12:40:07.713578Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning.arXiv preprint arXiv:2503.00223, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.24105","last_updated":"2025-05-30T01:13:22Z","snapshot_observed_at":"2026-08-09T13:46:57.143129Z","submitted_at":"2025-05-30T01:13:22Z","title":"Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:40:07.713578Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2505.24105"},"observation_digest":"sha256:0f01a26401490370378f2cc07113bfbed0494729945fb10b777dee7a1790282a","observation_id":"1ad0381e-b8c5-4f9d-a42d-14fa07e5d01c","resolution":{"observed_at":"2026-08-07T12:40:07.713578Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-07T11:16:58.570880Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.02911","last_updated":"2025-06-03T14:16:53Z","snapshot_observed_at":"2026-08-15T09:50:58.981115Z","submitted_at":"2025-06-03T14:16:53Z","title":"Cell-o1: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T11:16:58.570880Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2506.02911"},"observation_digest":"sha256:c4ddb7a9c29485390fbba85312a8704f302a7f5f39e8d24275e03cd90da5e5ef","observation_id":"456875cc-aab0-41bf-88a4-46e23986080c","resolution":{"observed_at":"2026-08-07T11:16:58.570880Z","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":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2506.08125","last_updated":"2026-04-16T20:47:09Z","snapshot_observed_at":"2026-08-16T04:52:06.297012Z","submitted_at":"2025-06-09T18:27:26Z","title":"Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning","version":4},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-19T10:06:05.697342Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2506.08125"},"observation_digest":"sha256:d5560355e86329c847a3722f6210c9048bf22a41c31d7823c8e5cbae33a98c28","observation_id":"d650c154-9b6a-47ec-97db-02ba1e517737","resolution":{"observed_at":"2026-05-19T10:07:14.205834Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2026-08-07T04:07:19.358923Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11603","last_updated":"2025-06-16T03:35:12Z","snapshot_observed_at":"2026-08-14T04:54:38.278933Z","submitted_at":"2025-06-13T09:17:36Z","title":"TongSearch-QR: Reinforced Query Reasoning for Retrieval","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T04:07:19.358923Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2506.11603"},"observation_digest":"sha256:49f3d35b36bd8e76971d7871f9461a9d57d487b2d93e6591ff77d56224dc7d4b","observation_id":"fbc2cd86-1f29-49da-81de-c5c269e04507","resolution":{"observed_at":"2026-08-07T04:07:19.358923Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-06T23:26:56.686867Z","title":"Deepretrieval: Powerful query generation for information retrieval with reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.18096","last_updated":"2025-09-03T15:32:23Z","snapshot_observed_at":"2026-08-15T20:12:18.032530Z","submitted_at":"2025-06-22T16:52:48Z","title":"Deep Research Agents: A Systematic Examination And Roadmap","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T23:26:56.686867Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2506.18096"},"observation_digest":"sha256:b292cd985365cbe6fc0fb593b49e1672ddb07f433802530e5be4e5113717718a","observation_id":"a362af34-c1ab-407d-a5c7-509a9f7d5b3e","resolution":{"observed_at":"2026-08-06T23:26:56.686867Z","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":"2026-08-16T12:54:07.632961Z","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":"sha256:7297ca9e391cc6266627c5b31851b083cbd2749532b81f7accd34a333f271046","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":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2026-08-06T20:01:29.098275Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.04069","last_updated":"2025-07-05T15:10:12Z","snapshot_observed_at":"2026-08-13T00:16:37.743443Z","submitted_at":"2025-07-05T15:10:12Z","title":"Beyond Independent Passages: Adaptive Passage Combination Retrieval for Retrieval Augmented Open-Domain Question Answering","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T20:01:29.098275Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2507.04069"},"observation_digest":"sha256:3c14cde4eaaf1cd7688103f7fa757f121d9f5a90bf0fcdbb017d80172e4dc80c","observation_id":"26795ec2-f6b8-4c4a-aca3-55d9da057ccd","resolution":{"observed_at":"2026-08-06T20:01:29.098275Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-06T17:49:48.879997Z","title":"arXiv preprint arXiv:2503.00223","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.09884","last_updated":"2025-07-26T11:17:08Z","snapshot_observed_at":"2026-08-17T20:47:45.997834Z","submitted_at":"2025-07-14T03:45:24Z","title":"VerifyBench: A Systematic Benchmark for Evaluating Reasoning Verifiers Across Domains","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T17:49:48.879997Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2507.09884"},"observation_digest":"sha256:fe0703a91504aab3a941123ad28d6f405e3debc562e54c2f1e531848cb6984cc","observation_id":"7070c892-62b0-4f54-98b4-5e5e37f5b15e","resolution":{"observed_at":"2026-08-06T17:49:48.879997Z","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":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"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":275,"source":"pdf_text","source_observed_at":"2026-05-18T19:19:36.427337Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2509.02547"},"observation_digest":"sha256:e73a3a957f403020d4422b73c7737156cdf641394aa15131606bb5f4e4b29ef1","observation_id":"7cf67020-c945-4a84-9d36-f214391e2de9","resolution":{"observed_at":"2026-05-18T19:21:46.705082Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2026-08-15T16:30:36.849031Z","title":"Bowen Jin, Hansi Zeng, Zhenrui Yue, Jinsung Yoon, Sercan Arik, Dong Wang, Hamed Zamani, and Jiawei Han","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.04820","last_updated":"2025-09-05T05:44:50Z","snapshot_observed_at":"2026-08-17T10:59:43.879283Z","submitted_at":"2025-09-05T05:44:50Z","title":"Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T16:30:36.849031Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2509.04820"},"observation_digest":"sha256:766280c88d9f5f0cb5969214aae691b5d360504f4120d043f316468d01e22322","observation_id":"48412180-b15a-4344-b498-059962a9d84f","resolution":{"observed_at":"2026-08-15T16:30:36.849031Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-04T23:23:40.444325Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.06650","last_updated":"2025-09-08T13:04:07Z","snapshot_observed_at":"2026-08-15T01:46:49.069429Z","submitted_at":"2025-09-08T13:04:07Z","title":"Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:40.444325Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2509.06650"},"observation_digest":"sha256:2603931ff50b8d22b3316c24fe58565297471f1b29e70487290f18cd91e03a78","observation_id":"d2e0ec60-d36f-407e-9bd4-7b6ae20e19bb","resolution":{"observed_at":"2026-08-04T23:23:40.444325Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-04T22:56:05.188630Z","title":"Jiang, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.06949","last_updated":"2025-09-08T17:58:06Z","snapshot_observed_at":"2026-08-14T20:38:08.942762Z","submitted_at":"2025-09-08T17:58:06Z","title":"Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T22:56:05.188630Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2509.06949"},"observation_digest":"sha256:8386fdded58e4610b9ccf081bea26e4e987ea193002b21d7f043323307144e52","observation_id":"dee62877-ebc6-4646-bf44-2f95bb3a51bc","resolution":{"observed_at":"2026-08-04T22:56:05.188630Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-15T15:56:21.378508Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.10697","last_updated":"2025-09-12T21:25:25Z","snapshot_observed_at":"2026-08-18T00:13:50.452715Z","submitted_at":"2025-09-12T21:25:25Z","title":"A Survey on Retrieval And Structuring Augmented Generation with Large Language Models","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-15T15:56:21.378508Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2509.10697"},"observation_digest":"sha256:47f1911d074ea55124e70a3c1d3d10abeb81a11a58e659485b8e2a9668d5fc5d","observation_id":"a5189bcf-0271-46d0-9733-87846b9b63da","resolution":{"observed_at":"2026-08-15T15:56:21.378508Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-04T09:08:01.942254Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.17139","last_updated":"2026-06-30T04:37:56Z","snapshot_observed_at":"2026-08-16T19:57:07.430559Z","submitted_at":"2025-10-20T04:16:28Z","title":"Rethinking On-policy Optimization for Query Augmentation","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-04T09:08:01.942254Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2510.17139"},"observation_digest":"sha256:7bd8a4b952aa77b00c8f4514dad655f10db317587970574b884b25143ebc3a0c","observation_id":"4cd9a041-d100-4fb8-81e6-9a0f83cb8368","resolution":{"observed_at":"2026-08-04T09:08:01.942254Z","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":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2601.12538","last_updated":"2026-01-18T18:58:23Z","snapshot_observed_at":"2026-08-04T22:42:23.171653Z","submitted_at":"2026-01-18T18:58:23Z","title":"Agentic Reasoning for Large Language Models","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-17T15:14:25.558878Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2601.12538"},"observation_digest":"sha256:5c7c2b5ecd4f84c44b9ec3067b2549df00bbc86bf4471e418c9ac1398d55746b","observation_id":"8101ce10-2b7b-4ad2-ad1d-c76b4a8d9052","resolution":{"observed_at":"2026-05-17T15:14:25.851932Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2601.21257","last_updated":"2026-04-19T21:04:27Z","snapshot_observed_at":"2026-08-11T02:55:47.386292Z","submitted_at":"2026-01-29T04:36:52Z","title":"MoCo: A One-Stop Shop for Model Collaboration Research","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-16T10:17:37.129753Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2601.21257"},"observation_digest":"sha256:756931602249983caaa0f6ca2d1e6018b02e6be80dd8a7f4282a493dac246503","observation_id":"5cd8013a-c711-43b9-8815-4a4fe2f0de6c","resolution":{"observed_at":"2026-05-16T10:17:43.691021Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2604.05818","last_updated":"2026-04-14T13:54:15Z","snapshot_observed_at":"2026-08-13T08:24:31.709698Z","submitted_at":"2026-04-07T12:52:38Z","title":"WikiSeeker: Rethinking the Role of Vision-Language Models in Knowledge-Based Visual Question Answering","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-10T19:59:10.657346Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2604.05818"},"observation_digest":"sha256:6e5a90a3b39742cb2f70b8e3d83005aae76155d7ffb6edfd32f0367577808d74","observation_id":"03f8cb0f-7b62-45c9-ae79-45eb3c5d24d0","resolution":{"observed_at":"2026-05-10T22:20:48.069237Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2604.07201","last_updated":"2026-04-08T15:28:21Z","snapshot_observed_at":"2026-08-13T01:38:15.603876Z","submitted_at":"2026-04-08T15:28:21Z","title":"BRIDGE: Multimodal-to-Text Retrieval via Reinforcement-Learned Query Alignment","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T17:28:59.838565Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2604.07201"},"observation_digest":"sha256:ce7eefe6933d06e4571b5d8c16f0f948fd20e28f3bfbe2f76ccc2e38992bae15","observation_id":"05338bfd-0bef-4c4f-9d79-39314030f58b","resolution":{"observed_at":"2026-05-11T06:41:58.185271Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2605.00505","last_updated":"2026-05-17T17:18:53Z","snapshot_observed_at":"2026-08-15T16:37:00.677209Z","submitted_at":"2026-05-01T08:30:52Z","title":"LLM-Oriented Information Retrieval: A Denoising-First Perspective","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-05-09T18:54:06.144968Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2605.00505"},"observation_digest":"sha256:2c386c0a40f4eac162ad07f7bde66ab417cf8ae4006703787b63b828b4764756","observation_id":"e46525f4-e212-48a2-b528-17297f55bcef","resolution":{"observed_at":"2026-05-11T16:01:19.410968Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2605.00505","last_updated":"2026-05-17T17:18:53Z","snapshot_observed_at":"2026-08-15T16:37:00.677209Z","submitted_at":"2026-05-01T08:30:52Z","title":"LLM-Oriented Information Retrieval: A Denoising-First Perspective","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-05-21T00:18:32.423103Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2605.00505"},"observation_digest":"sha256:0457e233e577ea8eb6b59000811ab48aef6cdd73dba390213ed2f5c470353e3b","observation_id":"16da76d2-a5d4-48d5-8b8b-3ef158b9fec5","resolution":{"observed_at":"2026-05-21T00:19:16.635713Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2605.00560","last_updated":"2026-05-01T10:58:18Z","snapshot_observed_at":"2026-08-14T15:42:00.719068Z","submitted_at":"2026-05-01T10:58:18Z","title":"When More Reformulations Hurt: Avoiding Drift using Ranker Feedback","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-09T18:40:35.840350Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2605.00560"},"observation_digest":"sha256:924503cc7a32c543317e92871704f2a7f2238db9d94f1c607b2f4e4d9f3c023e","observation_id":"a7ec09a6-cadd-477a-9e6e-4a2a4d894c5d","resolution":{"observed_at":"2026-05-09T19:35:39.582578Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2605.26352","last_updated":"2026-08-06T01:35:38Z","snapshot_observed_at":"2026-08-09T23:09:45.111534Z","submitted_at":"2026-05-25T21:56:29Z","title":"RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T21:24:11.882268Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2605.26352"},"observation_digest":"sha256:8714548f415eac88c62a8b25deabf35698611479de0e918c3a30143052e9ce58","observation_id":"94781733-9bd7-401c-b1a7-b464f600e30c","resolution":{"observed_at":"2026-06-29T21:43:59.842264Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2026-08-04T05:01:24.898991Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning.arXiv preprint arXiv:2503.00223, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.26352","last_updated":"2026-08-06T01:35:38Z","snapshot_observed_at":"2026-08-09T23:09:45.111534Z","submitted_at":"2026-05-25T21:56:29Z","title":"RICE-PO: Turning Retrieval Interactions into Credit Signals for Reasoning Agents","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T05:01:24.898991Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2605.26352"},"observation_digest":"sha256:632158113cf51297c555a643df663a5280e9eb7e08f9147d3ccb209c2be3f3a4","observation_id":"16c79743-9adf-4120-93e2-9c547587243c","resolution":{"observed_at":"2026-08-04T05:01:24.898991Z","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":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2606.02373","last_updated":"2026-06-01T15:21:41Z","snapshot_observed_at":"2026-08-14T09:20:36.323233Z","submitted_at":"2026-06-01T15:21:41Z","title":"Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-06-28T14:25:08.052988Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2606.02373"},"observation_digest":"sha256:3ad3f37b893ee2eef113fa776054c0a824e1dbf226c285ea4a6b8e780db0acdc","observation_id":"7dc89a95-3121-49e1-9d73-4dd2d5ec0194","resolution":{"observed_at":"2026-07-01T23:26:22.121774Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2606.07299","last_updated":"2026-06-05T14:10:48Z","snapshot_observed_at":"2026-08-02T12:02:14.138415Z","submitted_at":"2026-06-05T14:10:48Z","title":"DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T22:04:37.766752Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2606.07299"},"observation_digest":"sha256:53cf8ae0b84b9875cc470d83ef64349fab412a97c768db2680374599b6595e79","observation_id":"413b57ab-64db-4a4f-ba68-0b4b81f8cf15","resolution":{"observed_at":"2026-07-02T17:27:14.744673Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2606.12191","last_updated":"2026-06-10T15:15:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-06-10T15:15:01Z","title":"Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application","version":1},"reference_index":293,"source":"pdf_text","source_observed_at":"2026-06-27T09:46:30.702256Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2606.12191"},"observation_digest":"sha256:ccfedc550a2f5722f34e7a6f7c0479cc077abc76950c04ffb8bfa7f227778648","observation_id":"8de0e9af-6faf-41e1-a121-afbe97e9c5cf","resolution":{"observed_at":"2026-06-27T09:50:48.456275Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2606.27733","last_updated":"2026-08-11T07:28:23Z","snapshot_observed_at":"2026-08-14T23:09:33.946085Z","submitted_at":"2026-06-26T05:29:08Z","title":"BashCoder-R1: Towards Robust and Explainable Bash Code Generation with Robustness-Aware Group Relative Policy Optimization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T04:16:04.477464Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2606.27733"},"observation_digest":"sha256:203f0ca99af9e6128d572ccc2a2c12b33e7c4a11e9543890b9551543781a770b","observation_id":"77cd4fe3-a529-4362-ae5e-4be8937997a1","resolution":{"observed_at":"2026-07-01T17:05:50.881163Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2503.00223","doi":"10.48550/arxiv.2503.00223","metadata_source":"arxiv_reference","pith_arxiv_id":"2503.00223","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning","venue":"ArXiv.org","work_id":"9a13be45-57b7-49e8-8e18-06262212ada5","year":2025},"citing_paper":{"arxiv_id":"2606.28566","last_updated":"2026-06-26T19:44:34Z","snapshot_observed_at":"2026-08-16T23:16:29.087033Z","submitted_at":"2026-06-26T19:44:34Z","title":"R$^2$-Searcher: Calibrating Retrieval and Reasoning Boundaries for Agentic Search","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-30T00:33:59.778294Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2606.28566"},"observation_digest":"sha256:b0b0a9b5431ba2e164d70849965bff2cd780a2a4c496ed5280ddf4a435158134","observation_id":"36fbf8c0-1da7-4760-bb34-e4b8e2b4e1f5","resolution":{"observed_at":"2026-06-30T00:34:05.163437Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","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":"2026-08-04T01:47:05.477346Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.00030","last_updated":"2026-07-15T11:11:39Z","snapshot_observed_at":"2026-08-11T12:00:25.862129Z","submitted_at":"2026-07-15T11:11:39Z","title":"SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T01:47:05.477346Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2608.00030"},"observation_digest":"sha256:0161577eb981a1b88e861483bfe3e38732b3f1d83b3beeab039c795946aa3d63","observation_id":"2bdcbb89-4ab5-40b4-a29a-d9886aa44c2b","resolution":{"observed_at":"2026-08-04T01:47:05.477346Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-04T07:50:50.618709Z","title":"Deepretrieval: Hacking real search engines and retrievers with large language models via reinforcement learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02407","last_updated":"2026-08-03T15:49:14Z","snapshot_observed_at":"2026-08-16T21:18:38.451065Z","submitted_at":"2026-08-03T15:49:14Z","title":"Antares: Foundation Models for Agentic Vulnerability Localization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T07:50:50.618709Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2608.02407"},"observation_digest":"sha256:731169c5662b4b4ddebfda68fd56a01b280fae19f48895d2a8af3ceb93a83d49","observation_id":"4ac43929-65a7-4c0b-a76d-0c1212d43599","resolution":{"observed_at":"2026-08-04T07:50:50.618709Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-07T14:27:44.330205Z","title":"arXiv preprint arXiv:2503.00223 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06128","last_updated":"2026-08-11T02:08:54Z","snapshot_observed_at":"2026-08-17T20:26:27.360517Z","submitted_at":"2026-08-06T15:01:29Z","title":"Contextual Information Policy Optimization for Search Agents","version":1},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-07T14:27:44.330205Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2608.06128"},"observation_digest":"sha256:d86d88d1256eb6ee696f74439de26e12532ef465725d7431af7bee8109ebc420","observation_id":"668a14fe-e0cd-440c-8854-cb92036753e2","resolution":{"observed_at":"2026-08-07T14:27:44.330205Z","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":"2026-08-16T12:54:07.632961Z","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":"2026-08-12T00:54:15.264777Z","title":"arXiv preprint arXiv:2503.00223 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.06128","last_updated":"2026-08-11T02:08:54Z","snapshot_observed_at":"2026-08-17T20:26:27.360517Z","submitted_at":"2026-08-06T15:01:29Z","title":"Contextual Information Policy Optimization for Search Agents","version":3},"reference_index":83,"source":"arxiv_source","source_observed_at":"2026-08-12T00:54:15.264777Z"},"links":{"cited_paper":"/paper/2503.00223","citing_paper":"/paper/2608.06128"},"observation_digest":"sha256:3f6560f6f7f55e9a74c8eeae33f4bb07245b6cde13c156a693c5c01a6e189f75","observation_id":"e9d89d0f-0a8a-4bc9-a019-463776c6bda5","resolution":{"observed_at":"2026-08-12T00:54:15.264777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2503.00223/citation-record","integrity":"/paper/2503.00223/integrity","json":"/paper/2503.00223/citation-record.json","paper":"/paper/2503.00223"},"outbound":[],"paper":{"arxiv_id":"2503.00223","last_updated":"2025-04-12T03:17:26Z","latest_version":3,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-16T12:54:07.632961Z","submitted_at":"2025-02-28T22:16:42Z","title":"DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 37 inbound Pith citation observations for arXiv:2503.00223."}