{"as_of":"2026-08-07T18:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d454915baa833b8a785fede813be2ab306cf8c0e040aa4a2349a0f2d17f7b801","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:24:15.002601Z","state":"measured"},{"denominator":80,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":80,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:23:55.127390Z","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":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.14234","snapshot_observed_at":"2026-08-06T12:23:55.127390Z","title":"T.; Rahman, S.; Morol, M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.21836","last_updated":"2025-07-29T14:12:28Z","snapshot_observed_at":"2026-08-06T12:23:53.267663Z","submitted_at":"2025-07-29T14:12:28Z","title":"AutoTIR: Autonomous Tools Integrated Reasoning via Reinforcement Learning","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T12:23:55.127390Z"},"links":{"cited_paper":"/paper/2506.14234","citing_paper":"/paper/2507.21836"},"observation_digest":"sha256:a910e67e7b8e105490868e9ed872e30a363fa8f15c77f37ad02687494a5226d9","observation_id":"6c731bc9-f4b8-4805-8e1e-c245c8c2aa3c","resolution":{"observed_at":"2026-08-06T12:23:55.127390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"cited_work":{"arxiv_id":"2506.14234","doi":"10.48550/arxiv.2506.14234","metadata_source":"arxiv_reference","pith_arxiv_id":"2506.14234","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tanzib Hosain, Salman Rahman, Md","venue":"ArXiv.org","work_id":"640eec28-a0aa-41c7-bb9f-60ca3f4d2bc1","year":2025},"citing_paper":{"arxiv_id":"2511.00181","last_updated":"2026-04-07T09:06:09Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-10-31T18:36:49Z","title":"From Evidence to Verdict: An Agent-Based Forensic Framework for AI-Generated Image Detection","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-18T02:04:49.742067Z"},"links":{"cited_paper":"/paper/2506.14234","citing_paper":"/paper/2511.00181"},"observation_digest":"sha256:e0856a958f0809c817a337f94eb0426bef120e8d02529441f6b331104463c7f7","observation_id":"db0c3d42-5bf1-445f-8288-fbe1cc7b4189","resolution":{"observed_at":"2026-05-18T02:05:38.942110Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"cited_work":{"arxiv_id":"2506.14234","doi":"10.48550/arxiv.2506.14234","metadata_source":"arxiv_reference","pith_arxiv_id":"2506.14234","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tanzib Hosain, Salman Rahman, Md","venue":"ArXiv.org","work_id":"640eec28-a0aa-41c7-bb9f-60ca3f4d2bc1","year":2025},"citing_paper":{"arxiv_id":"2605.27816","last_updated":"2026-05-27T01:17:30Z","snapshot_observed_at":"2026-08-07T02:14:40.830938Z","submitted_at":"2026-05-27T01:17:30Z","title":"Pattern Recognition Tasks with Personalized Federated Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-29T14:07:13.510875Z"},"links":{"cited_paper":"/paper/2506.14234","citing_paper":"/paper/2605.27816"},"observation_digest":"sha256:d605d9662c3e16f181da066c798ed731b39bd30203a072515052949860aa3486","observation_id":"d1b24de5-4a16-4bae-ae75-b83d6c0c5a1f","resolution":{"observed_at":"2026-06-29T14:13:29.768400Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"cited_work":{"arxiv_id":"2506.14234","doi":"10.48550/arxiv.2506.14234","metadata_source":"arxiv_reference","pith_arxiv_id":"2506.14234","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tanzib Hosain, Salman Rahman, Md","venue":"ArXiv.org","work_id":"640eec28-a0aa-41c7-bb9f-60ca3f4d2bc1","year":2025},"citing_paper":{"arxiv_id":"2606.22385","last_updated":"2026-06-21T08:22:23Z","snapshot_observed_at":"2026-08-01T18:23:09.613202Z","submitted_at":"2026-06-21T08:22:23Z","title":"MetaPS: Adaptive Programmatic Strategy Selection for Market Agents","version":1},"reference_index":124,"source":"arxiv_source","source_observed_at":"2026-06-26T11:06:28.690956Z"},"links":{"cited_paper":"/paper/2506.14234","citing_paper":"/paper/2606.22385"},"observation_digest":"sha256:f9cbe6b615ef45a7d6f7b7166051df6408c07fddb853c03fca074c55f9297618","observation_id":"3ed881fd-fd8b-4a38-8b9c-1a7c3d1b42c0","resolution":{"observed_at":"2026-07-04T08:39:42.624129Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"cited_work":{"arxiv_id":"2506.14234","doi":"10.48550/arxiv.2506.14234","metadata_source":"arxiv_reference","pith_arxiv_id":"2506.14234","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Tanzib Hosain, Salman Rahman, Md","venue":"ArXiv.org","work_id":"640eec28-a0aa-41c7-bb9f-60ca3f4d2bc1","year":2025},"citing_paper":{"arxiv_id":"2606.24428","last_updated":"2026-06-23T11:05:05Z","snapshot_observed_at":"2026-08-05T04:36:12.703717Z","submitted_at":"2026-06-23T11:05:05Z","title":"Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-06-25T23:49:38.932474Z"},"links":{"cited_paper":"/paper/2506.14234","citing_paper":"/paper/2606.24428"},"observation_digest":"sha256:25aec38e481a4994f116480937eb76bfcb77bdb9e8389c7936e94a7f7a595ed2","observation_id":"038d2672-48f2-4123-aa07-c801c6d7e6cd","resolution":{"observed_at":"2026-07-04T17:20:00.011029Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.14234/citation-record","integrity":"/paper/2506.14234/integrity","json":"/paper/2506.14234/citation-record.json","paper":"/paper/2506.14234"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.01943","last_updated":"2025-08-07T23:04:55Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-02T17:50:31Z","title":"OpenCodeReasoning: Advancing Data Distillation for Competitive Coding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.01943","snapshot_observed_at":"2026-08-07T00:24:14.685913Z","title":"Opencodereasoning: Advancing data distillation for competitive coding, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.685913Z"},"links":{"cited_paper":"/paper/2504.01943","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:7d3f38f6589e9f4069e1713f4e00114e27a84aadcf17ac5d06b59d8147edbc78","observation_id":"6dada8f0-83c4-4017-be55-270078263ca7","resolution":{"observed_at":"2026-08-07T00:24:14.685913Z","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":"2026-08-07T00:24:16.052528Z","title":"Claude 3.7 Sonnet, 2025","venue":null,"work_id":"6acf347d-33e0-49cc-955b-8b3e4debae3b","year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.690967Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:0b57dd19453fe38713eae47ff4b794a635b1672afbe453b13c717a978e1430c5","observation_id":"0c614cfe-0377-49cd-9bdb-0003dda5b9fd","resolution":{"observed_at":"2026-08-07T00:24:16.056513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07732","last_updated":"2021-08-16T03:57:30Z","snapshot_observed_at":"2026-08-02T19:23:53.535075Z","submitted_at":"2021-08-16T03:57:30Z","title":"Program Synthesis with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07732","snapshot_observed_at":"2026-08-07T00:24:14.695286Z","title":"Program synthesis with large language models.arXiv preprint arXiv:2108.07732, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.695286Z"},"links":{"cited_paper":"/paper/2108.07732","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:2d75f006cf8fdf8c3320676ab43179c51dc0041c317d9672a827e624f70a7357","observation_id":"819a4718-773a-4f62-9f45-eec908982a71","resolution":{"observed_at":"2026-08-07T00:24:14.695286Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.700237Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.700237Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:5401fca957e37f622c69d72a25d58066185b3cd7432a4cfd37b5d7d94bb1f9dc","observation_id":"a2d62f35-e588-42a8-9fd8-1836b308f47e","resolution":{"observed_at":"2026-08-07T00:24:14.700237Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.10848","last_updated":"2023-10-23T05:05:15Z","snapshot_observed_at":"2026-07-06T16:08:37.443746Z","submitted_at":"2023-08-21T16:47:11Z","title":"AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.10848","snapshot_observed_at":"2026-08-07T00:24:14.704582Z","title":"Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents.arXiv preprint arXiv:2308.10848, 2(4):6, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.704582Z"},"links":{"cited_paper":"/paper/2308.10848","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:c67a6734fd6ce2d02390dfae34da29badc1bc2d8e916797a8c518d9fad24deeb","observation_id":"e22ae7c6-cdf6-4854-8c7b-0992b3c22e55","resolution":{"observed_at":"2026-08-07T00:24:14.704582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-07T01:45:38.840969Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-07T00:24:14.709621Z","title":"Training verifiers to solve math word problems.arXiv preprint arXiv:2110.14168, 2021","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.709621Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:8e621df953fa95919e0a1deb7d9f4dcc05bea571dd41c72c6dee861863cac5ac","observation_id":"0a4dac31-a18f-4590-9361-716a4bc0b4a8","resolution":{"observed_at":"2026-08-07T00:24:14.709621Z","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":"2026-08-07T00:24:16.030608Z","title":"Gemini 2.5, 2025","venue":null,"work_id":"6a478307-c801-4c09-b14e-3e1c581fcdff","year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.713795Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:7379d04f9454ca0b7657fe2810440dfcbaaee812ce1f632426d17882f2e63714","observation_id":"69df6864-fb2e-41c9-8eab-838e31d29fa5","resolution":{"observed_at":"2026-08-07T00:24:16.034884Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.718454Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.718454Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:3126ec3d7cb21678baf48c4736cb265fa4c7afa8e4fd21935e6ca9f41c57b0a9","observation_id":"2d82cbb1-1e1b-4715-8e9d-57a01c95bebc","resolution":{"observed_at":"2026-08-07T00:24:14.718454Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.14325","last_updated":"2023-05-23T17:55:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-23T17:55:11Z","title":"Improving Factuality and Reasoning in Language Models through Multiagent Debate","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.14325","snapshot_observed_at":"2026-08-07T00:24:14.723062Z","title":"Im- proving factuality and reasoning in language models through multiagent debate.arXiv preprint arXiv:2305.14325, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.723062Z"},"links":{"cited_paper":"/paper/2305.14325","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:ad86c09ddd6ba7f9007f2ca39344fb55475de68d28efc4109d346db7b285586d","observation_id":"42a667ce-e449-45d0-ba15-feedfbdf9b35","resolution":{"observed_at":"2026-08-07T00:24:14.723062Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04506","last_updated":"2025-02-06T21:13:44Z","snapshot_observed_at":"2026-08-07T17:42:37.442236Z","submitted_at":"2025-02-06T21:13:44Z","title":"When One LLM Drools, Multi-LLM Collaboration Rules","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04506","snapshot_observed_at":"2026-08-07T00:24:14.727426Z","title":"When one llm drools, multi-llm collaboration rules.ArXiv, abs/2502.04506, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.727426Z"},"links":{"cited_paper":"/paper/2502.04506","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:6b0346c3991b0f0fa74c212906bcba01857c6dd4ed1767b01dea8e62530ae6ee","observation_id":"23915f7d-c761-4521-afa8-1fcd3a59be27","resolution":{"observed_at":"2026-08-07T00:24:14.727426Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.732097Z","title":"Pal: Program-aided language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.732097Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:5a99b9cc2fcada2d3d36afc192e43aaf667cbd4582dd6bcf1a180dcc258d063a","observation_id":"f7e83c5b-2e21-4c93-869b-50950cced3e0","resolution":{"observed_at":"2026-08-07T00:24:14.732097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.04519","last_updated":"2025-01-08T14:12:57Z","snapshot_observed_at":"2026-08-04T20:57:22.329262Z","submitted_at":"2025-01-08T14:12:57Z","title":"rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.04519","snapshot_observed_at":"2026-08-07T00:24:14.735972Z","title":"rstar-math: Small llms can master math reasoning with self-evolved deep thinking, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.735972Z"},"links":{"cited_paper":"/paper/2501.04519","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:cfbc1359571e80f95c5b472ddcf301e6155902de57eebd63d1ce2138ad1b6c3d","observation_id":"05224726-4f85-4934-aa07-8d6c13110327","resolution":{"observed_at":"2026-08-07T00:24:14.735972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01680","last_updated":"2024-04-19T01:15:16Z","snapshot_observed_at":"2026-08-06T19:10:15.466826Z","submitted_at":"2024-01-21T23:36:14Z","title":"Large Language Model based Multi-Agents: A Survey of Progress and Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.01680","snapshot_observed_at":"2026-08-07T00:24:14.740298Z","title":"Large language model based multi-agents: A survey of progress and challenges.arXiv preprint arXiv:2402.01680, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.740298Z"},"links":{"cited_paper":"/paper/2402.01680","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:708a37bdc78dc6609fb37a8ae983e1d8a82a092edff0d2efaa6d4097f519e582","observation_id":"ee30c81f-4607-456d-896a-e8ce8d693654","resolution":{"observed_at":"2026-08-07T00:24:14.740298Z","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":"4938.35253","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:15.578100Z","title":"REALM: Retrieval-augmented language model pre-training","venue":null,"work_id":"f6aff156-2ac0-41ae-9e57-9af7ed437ffe","year":2020},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.744892Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:096855fc21b6a1bd6726b9c29b0d151753d469c04a050e8eb534c6e0eb4fd01a","observation_id":"bbdd7b2a-40a4-4553-ae5a-4a39f45cc861","resolution":{"observed_at":"2026-08-07T00:24:15.584929Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.749543Z","title":"Measuring mathematical problem solving with the math dataset","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.749543Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:910d24baa657e5cd06eb211be3fafc37517c9f3a5e459bf629d7f4c4f110afc3","observation_id":"7d7eb2a5-0aab-48ec-a691-c570099d8a73","resolution":{"observed_at":"2026-08-07T00:24:14.749543Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.00352","last_updated":"2024-11-01T14:36:52Z","snapshot_observed_at":"2026-07-06T16:01:07.532053Z","submitted_at":"2023-08-01T07:49:10Z","title":"MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.00352","snapshot_observed_at":"2026-08-07T00:24:14.753749Z","title":"Metagpt: Meta programming for multi-agent collaborative framework.arXiv preprint arXiv:2308.00352, 3(4):6, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.753749Z"},"links":{"cited_paper":"/paper/2308.00352","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:8ec8ddd5c429a621efe6da55711571c47a89d45d180438f4ae8bac8127dfcf48","observation_id":"c26ebfef-f9c4-44b2-a3a9-b65c834b8f6f","resolution":{"observed_at":"2026-08-07T00:24:14.753749Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.757983Z","title":"Ashraful Islam, Mohammed Eunus Ali, and Md Rizwan Parvez","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.757983Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:3ef8d977a9ca0842188e3d3c7472d915b879c35a597c1f2589f7fd2433fca39f","observation_id":"0d8ca278-08f4-4d5d-b99e-fef339d8dd60","resolution":{"observed_at":"2026-08-07T00:24:14.757983Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.761872Z","title":"Ashraful Islam, Mohammed Eunus Ali, and Md Rizwan Parvez","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.761872Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:79d57c61c7a3dad127ebf2f5dda618dc821d5f552842911fea5fbad58223ebc2","observation_id":"bd98ed11-af32-45d9-b728-8207799eaf3e","resolution":{"observed_at":"2026-08-07T00:24:14.761872Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.766050Z","title":"Open-RAG: Enhanced retrieval augmented reasoning with open-source large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.766050Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:73c9016659635db5d349825c3f77ad1db03d8d8d6f637dedfb49b11f26bc4d5b","observation_id":"260c8893-0754-4ea7-8691-84911dd32639","resolution":{"observed_at":"2026-08-07T00:24:14.766050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07974","last_updated":"2024-06-06T17:41:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-03-12T17:58:04Z","title":"LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07974","snapshot_observed_at":"2026-08-07T00:24:14.774440Z","title":"Livecodebench: Holistic and contamination free evaluation of large language models for code.arXiv preprint arXiv:2403.07974, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.774440Z"},"links":{"cited_paper":"/paper/2403.07974","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:db09fca47b418926cbeb5b284bce1d4090abc646dddd890d8392e47464d34855","observation_id":"5c6d86c7-331f-4217-9b69-3bed237e7baa","resolution":{"observed_at":"2026-08-07T00:24:14.774440Z","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":"2026-08-07T00:24:15.979808Z","title":"XCodeEval: An execution-based large scale multilingual multitask bench- mark for code understanding, generation, translation and retrieval","venue":null,"work_id":"e9ecbae9-2d54-476f-a21d-9cdff8d087d3","year":null},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.778694Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:edfed6e0d158f6bf96e5b25701a0829b9775a87a44d64cf4ad2dbbb3cb68c937","observation_id":"44dc2b46-7068-46ce-b6e2-151a82dd115f","resolution":{"observed_at":"2026-08-07T00:24:15.984537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.786554Z","title":"Kimi k1.5: Scaling reinforcement learning with llms, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.786554Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:6e6da16359a4d81738dff15e2c2335c265da90c4c2ea44cad0b7e2583a82f831","observation_id":"9789bd71-73f3-4c62-9164-0d03d6fc0e1f","resolution":{"observed_at":"2026-08-07T00:24:14.786554Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.790766Z","title":"A systematic survey and critical review on evaluating large language models: Challenges, limitations, and recommendations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.790766Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:54e07e09436da1c574ce221d3c25bff61ed0877618f84dc8a599aef8be66320e","observation_id":"47481de7-d756-4221-9be9-7173737d6d1a","resolution":{"observed_at":"2026-08-07T00:24:14.790766Z","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":"2026-08-07T00:24:15.950559Z","title":"Techniquerag: Retrieval augmented generation for adversarial technique annotation in cyber threat intelligence text,","venue":null,"work_id":"cea832d3-564a-4cc8-8827-10d3bfc214c1","year":null},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.795009Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:456f04eedb8a6006f4ae063dd94cc1bc01de7e166c4bf85a5802e80178d45761","observation_id":"db662056-c866-49d9-a394-9e3c49e2eb39","resolution":{"observed_at":"2026-08-07T00:24:15.955342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:15.937031Z","title":"Retrieval-augmented generation for knowledge-intensive nlp tasks.Advances in Neural Information Processing Systems, 33:9459–9474, 2020","venue":null,"work_id":"aa230fb9-69ce-431a-8f00-8c6cf2dd01b2","year":2020},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.804155Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:90aec0c2ddcfd7d6821fe7e4051672b7ef553eaf6f91ffd5c5b0509e962c00dc","observation_id":"9e45a3de-a8e3-4184-858f-4bf72d583df2","resolution":{"observed_at":"2026-08-07T00:24:15.941644Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:15.923558Z","title":"Solving quantitative reasoning problems with language models.Advances in Neural Information Processing Systems, 35: 3843–3857, 2022","venue":null,"work_id":"f064780b-b5d5-4977-be2d-d88c70534c6f","year":2022},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.808053Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:c34994775f2a38ac4426b164b303e455bba138f9575bb783b71c785694db8582","observation_id":"f6e31e6d-f3e8-4d36-af7e-7cf850625add","resolution":{"observed_at":"2026-08-07T00:24:15.927971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.811920Z","title":"Camel: Communicative agents for\" mind\" exploration of large language model society.Advances in Neural Information Processing Systems, 36:51991–52008, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.811920Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:b88e5f726693e86addbc2d7d7f9e0872d5f8a9f9c67347b13a7683caeef9a0d7","observation_id":"21530d56-553d-4b70-8ce4-9d267c703072","resolution":{"observed_at":"2026-08-07T00:24:14.811920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.05366","last_updated":"2025-01-09T16:48:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-09T16:48:17Z","title":"Search-o1: Agentic Search-Enhanced Large Reasoning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.05366","snapshot_observed_at":"2026-08-07T00:24:14.815853Z","title":"Search-o1: Agentic search-enhanced large reasoning models.arXiv preprint arXiv:2501.05366, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.815853Z"},"links":{"cited_paper":"/paper/2501.05366","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:d640bccd819d5cc9479c1538f51fc416f2d65b32fbf01f8fda1b7f3cebad232b","observation_id":"393f4a05-d426-4230-9415-fefb80b44ad7","resolution":{"observed_at":"2026-08-07T00:24:14.815853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-07T00:24:14.819912Z","title":"Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.819912Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:7341a4570ab7ff05e1b50ef34f06bbaaaf33e75ef01d1da851be7904663be4dd","observation_id":"caca195e-a324-41f0-88ab-2c7070b0c45b","resolution":{"observed_at":"2026-08-07T00:24:14.819912Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.21297","last_updated":"2025-05-27T15:00:57Z","snapshot_observed_at":"2026-08-07T13:28:17.822753Z","submitted_at":"2025-05-27T15:00:57Z","title":"rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.21297","snapshot_observed_at":"2026-08-07T00:24:14.823958Z","title":"rstar-coder: Scaling competitive code reasoning with a large-scale verified dataset.arXiv preprint arXiv:2505.21297, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.823958Z"},"links":{"cited_paper":"/paper/2505.21297","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:de36ebd1cbc4d8fa9a8b83a1001143e2d7f33d61c7bacc8ce8a7ad82d13b852a","observation_id":"5737eabc-8cf1-4b86-ad60-e01076865642","resolution":{"observed_at":"2026-08-07T00:24:14.823958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02170","last_updated":"2024-11-15T04:30:04Z","snapshot_observed_at":"2026-08-07T04:49:42.215176Z","submitted_at":"2023-10-03T16:05:48Z","title":"A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.02170","snapshot_observed_at":"2026-08-07T00:24:14.828288Z","title":"Dynamic llm-agent network: An llm-agent collaboration framework with agent team optimization.arXiv preprint arXiv:2310.02170, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.828288Z"},"links":{"cited_paper":"/paper/2310.02170","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:65dcdce9611f8198be37a3424d906fc04e9933918979a0abf1c50e5648fe5747","observation_id":"a537ec6d-8c0b-4e25-9ae7-1fa9990e4944","resolution":{"observed_at":"2026-08-07T00:24:14.828288Z","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":"2026-08-07T00:24:15.901910Z","title":"Chameleon: Plug-and-play compositional reasoning with large language models.Advances in Neural Information Processing Systems, 36, 2024","venue":null,"work_id":"016fb2dc-6c59-4fdb-911c-ed98417acdad","year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.833079Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:2568e6f60bdc285ae0653d9e2d8551dfdb05cf6bef5f340dc347a1341cacb298","observation_id":"96c6f384-8e2a-4c89-a72b-8949932b6c34","resolution":{"observed_at":"2026-08-07T00:24:15.906628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11271","last_updated":"2026-04-13T23:08:01Z","snapshot_observed_at":"2026-08-03T02:43:16.180683Z","submitted_at":"2025-02-16T21:18:47Z","title":"OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.11271","snapshot_observed_at":"2026-08-07T00:24:14.837321Z","title":"Octotools: An agentic framework with extensible tools for complex reasoning.arXiv preprint arXiv:2502.11271, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.837321Z"},"links":{"cited_paper":"/paper/2502.11271","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:f98ee7b581ffb55b0022de353eb2df885b189ca01a26fa0aba3e6bc5f40e784a","observation_id":"59a46066-6a72-4258-a56b-293e67a799ab","resolution":{"observed_at":"2026-08-07T00:24:14.837321Z","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":"2026-08-07T00:24:15.888975Z","title":"American invitational mathematics examination - aime","venue":null,"work_id":"50c65313-dbc2-4791-99ab-f5a58d33edd9","year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.841439Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:fa1c3fd53940deaa76389a7acaea855c4246172311f96a51f1685e0f1d3c163a","observation_id":"c326084a-fa11-423d-98f2-651e5231eff2","resolution":{"observed_at":"2026-08-07T00:24:15.893403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:15.875752Z","title":"American invitational mathematics examination - aime","venue":null,"work_id":"c6d5c8bf-426a-471b-9027-41bb6e32c096","year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.845829Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:259ca1a997aa0ca9bbb6cc602a5bcf86189fd6db52ffbfb4e1e933f2c33c0eb1","observation_id":"ccc528b1-a557-4240-a091-f7fa166c0a58","resolution":{"observed_at":"2026-08-07T00:24:15.880409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12832","last_updated":"2024-10-02T17:58:39Z","snapshot_observed_at":"2026-08-03T10:06:52.418096Z","submitted_at":"2024-10-02T17:58:39Z","title":"Generative Reward Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12832","snapshot_observed_at":"2026-08-07T00:24:14.849547Z","title":"Generative reward models.arXiv preprint arXiv:2410.12832, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.849547Z"},"links":{"cited_paper":"/paper/2410.12832","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:c28988741948f526e95fc28f8e0d054289574bc14d83257a9eff9296d001e225","observation_id":"939ef2b8-cf7a-4930-b81a-bd1b45c525ee","resolution":{"observed_at":"2026-08-07T00:24:14.849547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.16891","last_updated":"2025-04-23T17:13:04Z","snapshot_observed_at":"2026-08-07T15:59:48.302908Z","submitted_at":"2025-04-23T17:13:04Z","title":"AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.16891","snapshot_observed_at":"2026-08-07T00:24:14.853998Z","title":"Aimo-2 winning solution: Building state-of-the-art mathe- matical reasoning models with openmathreasoning dataset, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.853998Z"},"links":{"cited_paper":"/paper/2504.16891","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:601c9c816c90261f849067981632d3bc3962a1160563ed593a0d2ecca6d664be","observation_id":"da2f005b-5338-4cb3-b3c2-dd03aaa57e58","resolution":{"observed_at":"2026-08-07T00:24:14.853998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.09332","last_updated":"2022-06-01T19:08:11Z","snapshot_observed_at":"2026-08-07T17:14:39.278754Z","submitted_at":"2021-12-17T05:43:43Z","title":"WebGPT: Browser-assisted question-answering with human feedback","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.09332","snapshot_observed_at":"2026-08-07T00:24:14.858120Z","title":"Webgpt: Browser-assisted question-answering with human feedback.arXiv preprint arXiv:2112.09332, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.858120Z"},"links":{"cited_paper":"/paper/2112.09332","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:f766b666cc491bc1c56c769264fea1a5d6910ac2d9ae26cad143700759e3abbe","observation_id":"27b19d2f-92c8-4559-a354-19079240a252","resolution":{"observed_at":"2026-08-07T00:24:14.858120Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.00114","last_updated":"2021-11-30T21:32:46Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-11-30T21:32:46Z","title":"Show Your Work: Scratchpads for Intermediate Computation with Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.00114","snapshot_observed_at":"2026-08-07T00:24:14.862356Z","title":"Show your work: Scratchpads for intermediate computation with language models.arXiv preprint arXiv:2112.00114, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.862356Z"},"links":{"cited_paper":"/paper/2112.00114","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:3613704f04e4849632d86e66dfd46474593ebdd8d868fc8d7f429b130d69e7b6","observation_id":"3031909f-b158-4fff-8e68-9eac8f52ee30","resolution":{"observed_at":"2026-08-07T00:24:14.862356Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.866377Z","title":"Hello GPT-4o, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.866377Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:f976bd8926f14ebdb3f57d2f6122b0d69138081ac6697084ee22e10a82ab4c9b","observation_id":"ef2137a1-48a3-4799-89eb-bdc713a592ef","resolution":{"observed_at":"2026-08-07T00:24:14.866377Z","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":"2026-08-07T00:24:15.854154Z","title":"Learning to reason with llms, 2024","venue":null,"work_id":"d7ba25c8-18ff-4e47-b77d-0f11c2518839","year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.870705Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:4427fc47c36ae166327b2f8c9e8185b11ec733ca3c9dd09c27b87f39fdf74602","observation_id":"1a0be701-3de0-4e94-a4c4-81208268bee1","resolution":{"observed_at":"2026-08-07T00:24:15.858831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.874642Z","title":"Introducing openai o3 and o4-mini, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.874642Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:3e17ec67a593c5b350e555d4c0400f7ce0f4de46004e42051913e37d46c35a94","observation_id":"e42829f6-4a67-437c-902d-25118cb1f71b","resolution":{"observed_at":"2026-08-07T00:24:14.874642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08560","last_updated":"2024-02-12T18:59:46Z","snapshot_observed_at":"2026-08-03T06:31:15.541423Z","submitted_at":"2023-10-12T17:51:32Z","title":"MemGPT: Towards LLMs as Operating Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.08560","snapshot_observed_at":"2026-08-07T00:24:14.878575Z","title":"Patil, Kevin Lin, Sarah Wooders, and Joseph Gonzalez","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.878575Z"},"links":{"cited_paper":"/paper/2310.08560","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:8acf46bd185a8f1bd5f4c23ee39b58c7c250a3f13cd6cdbd86383ea1fb732b66","observation_id":"407ac7bf-fd53-4c6a-96d6-5374790a2ed2","resolution":{"observed_at":"2026-08-07T00:24:14.878575Z","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":"10.18653/v1/2025.kn","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:15.079450Z","title":"Chain of evidences and evidence to generate: Prompting for context grounded and retrieval augmented reasoning","venue":null,"work_id":"592f653a-1912-4021-8bcb-5e749283348f","year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.883130Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:0bf2d404e8746a0cc742bfb30b2d8f1a1a8494d1d904248d76646fbb9e200584","observation_id":"63171561-b1e3-4588-94d8-4a32691533a1","resolution":{"observed_at":"2026-08-07T00:24:15.083909Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.887025Z","title":"Evaluating the values of sources in transfer learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.887025Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:6cad95074522200c3f5c7677eb1edc310c87eeb6f0e34c7210b016ea85e0a0ca","observation_id":"2cf63cc0-00b2-41bf-bdf9-1718cbe3423c","resolution":{"observed_at":"2026-08-07T00:24:14.887025Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.890927Z","title":"Retrieval augmented code generation and summarization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.890927Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:cc21a4ae2c252ac61fc130fa08332b2feae2f53cecddb06a1aec00fe266099be","observation_id":"ff97a879-a419-4f3e-8cd3-b5cbc5ddcb2a","resolution":{"observed_at":"2026-08-07T00:24:14.890927Z","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":"10.18653/v1/2023.eacl-main.16","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:15.044812Z","title":"Retrieval enhanced data augmentation for question answering on privacy policies","venue":null,"work_id":"ddc4d248-3f7a-4865-a204-ecc49103ef5f","year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.895049Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:06d07a72743a827feb71e93b6d78057fbbc19fbfe83cff6cd0440ab8a5442341","observation_id":"2572e438-433f-4e82-a8a7-93c704986c9a","resolution":{"observed_at":"2026-08-07T00:24:15.050693Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:15.833240Z","title":"Qwq: Reflect deeply on the boundaries of the unknown, 2024","venue":null,"work_id":"466ec224-5e60-4185-a6da-cc96398e6f04","year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.899219Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:e74bb23242540eaba1ca1abff26dcaddcbe107fe91ce85e8e2f58d1ebfc232cb","observation_id":"f5f4a882-64b1-4f33-95be-e50d39f53a71","resolution":{"observed_at":"2026-08-07T00:24:15.837556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:15.820021Z","title":"QwQ-32B: Embracing the power of reinforcement learning, March 2025","venue":null,"work_id":"6454653c-ddad-4e77-8431-17e5ae5f0ee3","year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.903232Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:c7d9d747a888c7f6a4a375dcbfabf909194224157577e3bae9570d054e51b4de","observation_id":"ef186d05-c19c-4a34-badc-0584fa9e0f98","resolution":{"observed_at":"2026-08-07T00:24:15.824221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2506.02175","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:15.333929Z","title":"Ai debate aids assessment of controversial claims.arXiv preprint arXiv:2506.02175, 2025","venue":null,"work_id":"790e1e18-d67f-41bb-8b51-8b7ee4d80e1a","year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.907634Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:2cce012ceffa757d80dd2751012933eb23f84ba3004f22c36382d81d4d470bf1","observation_id":"75d68f2b-2618-4349-91a9-a064ea2f178d","resolution":{"observed_at":"2026-08-07T00:24:15.341144Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:15.806371Z","title":"DelucionQA: Detecting hallucinations in domain-specific question answering","venue":null,"work_id":"8caffa1f-d506-4aa3-8801-f23797f9db4d","year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.911777Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:fd71e866d8b7ea5bcef236fbe1fd49872aff466856d094b6494ad4eb502e3e52","observation_id":"c272234e-b034-49df-8148-821e11816b89","resolution":{"observed_at":"2026-08-07T00:24:15.811364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.920410Z","title":"Reflexion: Language agents with verbal reinforcement learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.920410Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:00b4d8291f457d630d027fff7e6397c7fdce64dd4a2912382694e332e3065364","observation_id":"92141737-9a7b-4051-8f42-e38a988114e9","resolution":{"observed_at":"2026-08-07T00:24:14.920410Z","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":"2026-08-07T00:24:15.783568Z","title":"Reflexion: Language agents with verbal reinforcement learning.Advances in Neural Information Processing Systems, 36, 2024","venue":null,"work_id":"8b6dcc9c-cc0c-4fff-b1a7-d5578e2814c9","year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.925306Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:69b28a52a580c21072cec03045529ecba21488784b7251fd3b71979044184606","observation_id":"f8c392f8-d169-492d-904f-d1f1cef9f3cd","resolution":{"observed_at":"2026-08-07T00:24:15.787845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:15.769765Z","title":"Tenenbaum, Antonio Torralba, Shuang Li, and Igor Mordatch","venue":null,"work_id":"a284526d-e2ee-4ee2-9a26-1c431662ced7","year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.929057Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:339b8357386671002202882d0c725640c95bd80f68d35ff920f6b0db69c8847c","observation_id":"b376defb-f332-40c1-820c-ea228e5eee1d","resolution":{"observed_at":"2026-08-07T00:24:15.775001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.07952","last_updated":"2025-04-10T17:57:33Z","snapshot_observed_at":"2026-08-07T16:06:45.454822Z","submitted_at":"2025-04-10T17:57:33Z","title":"Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.07952","snapshot_observed_at":"2026-08-07T00:24:14.933815Z","title":"Dynamic cheatsheet: Test-time learning with adaptive memory.arXiv preprint arXiv:2504.07952, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.933815Z"},"links":{"cited_paper":"/paper/2504.07952","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:9c71d737269eddf8e59daa8e1b457b6e76ea6a0425650bd03a1d92664fc13821","observation_id":"b391d688-c6fe-4d32-bbeb-343b0067b87b","resolution":{"observed_at":"2026-08-07T00:24:14.933815Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.937861Z","title":"Qwen3, April 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.937861Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:428e3c6539fec6f8cfba4deda5d29dd4aaa84f0c27443240555dcd25e0f2f529","observation_id":"82b7e2db-a626-43a5-b65d-c9ed057ace8e","resolution":{"observed_at":"2026-08-07T00:24:14.937861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-06T07:29:34.258050Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-07T00:24:14.942013Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset.arXiv preprint arXiv:2402.10176, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.942013Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:61f796f7959dd223ac259ba67728b759dc0a534f236d8cc48b7fb3d095854d3e","observation_id":"a5f8ebd3-369d-4967-8041-1895d8c2e60d","resolution":{"observed_at":"2026-08-07T00:24:14.942013Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.946771Z","title":"Self-consistency improves chain of thought reasoning in language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.946771Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:04efbe7534a639fd075ea0d82dff8cdcb120d0561bde2d3d3f30e1ace8def32b","observation_id":"d765a8b8-970f-4a08-8113-846bac0ff8d0","resolution":{"observed_at":"2026-08-07T00:24:14.946771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.11171","last_updated":"2023-03-07T17:57:37Z","snapshot_observed_at":"2026-07-06T12:50:22.773056Z","submitted_at":"2022-03-21T17:48:52Z","title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.11171","snapshot_observed_at":"2026-08-07T00:24:14.951204Z","title":"Self-consistency improves chain of thought reasoning in language models.ArXiv, abs/2203.11171, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.951204Z"},"links":{"cited_paper":"/paper/2203.11171","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:c51f284e0ea64378c716ef7e6075d7405743dca4b0f85da05ed2ac749708ed0b","observation_id":"704fca05-3593-45a0-abd5-9f88e608e979","resolution":{"observed_at":"2026-08-07T00:24:14.951204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.08377","last_updated":"2023-11-14T18:41:54Z","snapshot_observed_at":"2026-07-06T16:47:33.021833Z","submitted_at":"2023-11-14T18:41:54Z","title":"Learning to Filter Context for Retrieval-Augmented Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.08377","snapshot_observed_at":"2026-08-07T00:24:14.956100Z","title":"Learning to filter context for retrieval-augmented generation.arXiv preprint arXiv:2311.08377, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.956100Z"},"links":{"cited_paper":"/paper/2311.08377","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:5afb507861899ce690d653bfb3fa557d9b256e10eac6897496a6b22f265b4188","observation_id":"54c6d8ba-27e4-4b18-a929-9df6691706b0","resolution":{"observed_at":"2026-08-07T00:24:14.956100Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.960390Z","title":"Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.960390Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:f3e8757365e7eebac7d4b55949f7012f3b0ecf43b2569233a46b693facae88b4","observation_id":"d8ed9855-3fd9-47cd-b5e7-01bc9e6e818d","resolution":{"observed_at":"2026-08-07T00:24:14.960390Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.08155","last_updated":"2023-10-03T20:47:10Z","snapshot_observed_at":"2026-07-31T19:03:03.494918Z","submitted_at":"2023-08-16T05:57:52Z","title":"AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.08155","snapshot_observed_at":"2026-08-07T00:24:14.964319Z","title":"Autogen: Enabling next-gen llm applications via multi-agent conversation framework.arXiv preprint arXiv:2308.08155, 3(4), 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.964319Z"},"links":{"cited_paper":"/paper/2308.08155","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:b464843ccb30f5a0bada3b19d0f6b450806d75b0ac62c1ae9e475d04967b2120","observation_id":"ebc1e5d7-f471-483d-b26c-5d593802f8e9","resolution":{"observed_at":"2026-08-07T00:24:14.964319Z","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":"2026-08-07T00:24:15.732855Z","title":"Grok 3 beta — the age of reasoning agents, 2025","venue":null,"work_id":"b9e9014c-4efd-4aaa-9202-df76c7f93029","year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.969355Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:759369bb4ea5a687c62484a9a3b963a7f0cee66da984b0c7c73ecd09fc9ed77e","observation_id":"679754b3-460c-4d7e-b19f-38960e7ee7dd","resolution":{"observed_at":"2026-08-07T00:24:15.737008Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10601","last_updated":"2023-12-03T22:50:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-05-17T23:16:17Z","title":"Tree of Thoughts: Deliberate Problem Solving with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10601","snapshot_observed_at":"2026-08-07T00:24:14.973716Z","title":"Tree of thoughts: Deliberate problem solving with large language models.arXiv preprint arXiv:2305.10601, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.973716Z"},"links":{"cited_paper":"/paper/2305.10601","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:ea09621801096f12b8bca51acd633be5937ef28746ce914a59a6882e38e631ee","observation_id":"91d51960-9ac3-453f-9bfc-529dfa33e85b","resolution":{"observed_at":"2026-08-07T00:24:14.973716Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.977708Z","title":"React: Synergizing reasoning and acting in language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.977708Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:879fff034ecaf76ca2d0a8544b37c51f2cade6ce31f9ed1cfb11a0d479c71358","observation_id":"7a34bcd7-755c-44bf-8c9a-3f1f15ff57f9","resolution":{"observed_at":"2026-08-07T00:24:14.977708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.02151","last_updated":"2024-05-05T05:04:49Z","snapshot_observed_at":"2026-08-07T03:57:16.625123Z","submitted_at":"2023-08-04T06:14:23Z","title":"Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.02151","snapshot_observed_at":"2026-08-07T00:24:14.981510Z","title":"Retroformer: Retrospective large language agents with policy gradient op- timization.ArXiv, abs/2308.02151, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.981510Z"},"links":{"cited_paper":"/paper/2308.02151","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:c45e57ef45c9e31d799eb274d637add5397bd4d4d2418d0c7f9d4ffa8f2a2688","observation_id":"498ff026-fe3f-4f37-bd26-0a457ef52ece","resolution":{"observed_at":"2026-08-07T00:24:14.981510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01718","last_updated":"2025-05-24T04:36:48Z","snapshot_observed_at":"2026-07-06T20:30:31.588538Z","submitted_at":"2025-02-03T18:46:04Z","title":"ACECODER: Acing Coder RL via Automated Test-Case Synthesis","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01718","snapshot_observed_at":"2026-08-07T00:24:14.985905Z","title":"Acecoder: Acing coder rl via automated test-case synthesis.arXiv preprint arXiv:2502.01718, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.985905Z"},"links":{"cited_paper":"/paper/2502.01718","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:0292e13a4a1122b0d6f2c3dae5fd0b743891985d785fc40d56018135635d2338","observation_id":"c1338ff5-3cbe-4235-a45f-54061a680d6e","resolution":{"observed_at":"2026-08-07T00:24:14.985905Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.24863","last_updated":"2025-05-30T17:58:36Z","snapshot_observed_at":"2026-08-07T18:20:16.968814Z","submitted_at":"2025-05-30T17:58:36Z","title":"AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24863","snapshot_observed_at":"2026-08-07T00:24:14.990061Z","title":"Alphaone: Reasoning models thinking slow and fast at test time","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.990061Z"},"links":{"cited_paper":"/paper/2505.24863","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:18155d548145e318d565927faf03a26fe50ea6eb88f62b5c8226901e03b82234","observation_id":"875c14e5-9da1-4761-810f-29e2a32e649b","resolution":{"observed_at":"2026-08-07T00:24:14.990061Z","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":"2026-08-07T00:24:15.711678Z","title":"Liu, and Gao Huang","venue":null,"work_id":"9fe480ec-04ab-461b-9a56-d0d2a1a23aed","year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.994310Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:b5410d8006bb2d17fd4a1c1eb827ee6522af8d682135702b8df2fec406fc2d30","observation_id":"714a48db-e91c-48d7-aa9a-d08fcb72d09f","resolution":{"observed_at":"2026-08-07T00:24:15.716537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16906","last_updated":"2024-06-06T06:01:41Z","snapshot_observed_at":"2026-08-03T14:14:25.841503Z","submitted_at":"2024-02-25T00:56:27Z","title":"Debug like a Human: A Large Language Model Debugger via Verifying Runtime Execution Step-by-step","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16906","snapshot_observed_at":"2026-08-07T00:24:14.998105Z","title":"Ldb: A large language model debugger via verifying runtime execution step-by-step.arXiv preprint arXiv:2402.16906, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.998105Z"},"links":{"cited_paper":"/paper/2402.16906","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:0a7a03f60a9f0ba38640c1a56161a6a824805cabe6ce27bc65932390cd8dc9b8","observation_id":"f1a9f071-1258-4a48-8b0f-b0dc9ec1bbdf","resolution":{"observed_at":"2026-08-07T00:24:14.998105Z","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":"2026-08-07T00:24:15.695608Z","title":"twice as many pillow cases as sheets","venue":null,"work_id":"5f4f9423-1831-4ef7-a14b-a917ab57649f","year":null},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:15.002601Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:7bb00c2d259e8088b9263a1a6ceb29a2f621f846ef3a0330d76681df493213db","observation_id":"6c0e4126-0336-44e8-815d-d4ad6d36c272","resolution":{"observed_at":"2026-08-07T00:24:15.702204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:15.993938Z","title":null,"venue":null,"work_id":"914b13c5-8fb0-4730-837e-a97545d4f6ba","year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":831,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.770137Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:0238572b160b550681f6a927b5245c31cd4c0a9caa3c13d1994cf6bd4cf5e3ca","observation_id":"5c7927f6-7823-45d4-890e-44a5d331f519","resolution":{"observed_at":"2026-08-07T00:24:15.998154Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.916061Z","title":"doi: 10.18653/v1/2023.findings-emnlp.59","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.916061Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:54bc4879daf4da1fddcaca5a590832dcceb5ef0891a541b75e01174744c35432","observation_id":"99ae893e-3e1f-40ea-9437-60f6f7b2c990","resolution":{"observed_at":"2026-08-07T00:24:14.916061Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:24:14.782695Z","title":"doi: 10.18653/v1/2024.acl-long.367","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.782695Z"},"links":{"citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:c053a01798bcdf6933a8183a2f64b8e5fc283093ed7af6c57485a435f3f25260","observation_id":"d74fb48a-d5e5-410c-88b6-80ab613d724a","resolution":{"observed_at":"2026-08-07T00:24:14.782695Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.11988","last_updated":"2025-08-09T15:28:12Z","snapshot_observed_at":"2026-08-07T15:43:52.970544Z","submitted_at":"2025-05-17T12:46:10Z","title":"TechniqueRAG: Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text","version":2},"cited_work":{"arxiv_id":"2505.11988","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.11988","snapshot_observed_at":"2026-08-07T00:24:15.483226Z","title":"TechniqueRAG: Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text","venue":"cs.CR","work_id":"170c76d2-2e07-4b70-96f7-bae998237e04","year":2025},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.799168Z"},"links":{"cited_paper":"/paper/2505.11988","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:ae96bf7c5581bbd76fc02909bb0fbe397fd06dbfe38807745d455621675d9125","observation_id":"7b37a423-a7af-414e-991c-e452d8fd1b21","resolution":{"observed_at":"2026-08-07T00:24:15.487839Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T00:15:52.455187Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":52,"verified_exact":4,"verified_fuzzy":18},"total_outbound_references":75},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 5 inbound Pith citation observations for arXiv:2506.14234."}