{"as_of":"2026-08-10T18:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1582fde4b4b23dfd223544ba75f090f46b62cb352c991f1114bfb1cd59ab87cc","coverage":[{"denominator":56,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":56,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:37:09.232525Z","state":"measured"},{"denominator":73,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":73,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":17,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:49:36.120187Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-25T06:15:23.470485Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-08-07T05:49:36.120187Z","title":"Think only when you need with large hybrid-reasoning models, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07104","last_updated":"2025-09-10T09:03:04Z","snapshot_observed_at":"2026-08-10T18:05:29.816593Z","submitted_at":"2025-06-08T12:18:50Z","title":"How Far Are We from Optimal Reasoning Efficiency?","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:49:36.120187Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2506.07104"},"observation_digest":"sha256:07bef613ff57239ef92c5a33ce2280331ff5f514241feb2e0acd12657627029c","observation_id":"7b95b0bf-bfc5-4c36-a823-b52e471b94d0","resolution":{"observed_at":"2026-08-07T05:49:36.120187Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-08-07T01:05:26.835657Z","title":"Think only when you need with large hybrid-reasoning models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.11986","last_updated":"2025-06-13T17:46:02Z","snapshot_observed_at":"2026-08-10T10:04:27.415262Z","submitted_at":"2025-06-13T17:46:02Z","title":"Schema-R1: A reasoning training approach for schema linking in Text-to-SQL Task","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T01:05:26.835657Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2506.11986"},"observation_digest":"sha256:ff9afcee612b2ea8c8a53796c6940cf1c3688f58545554de19a94754e29baa2e","observation_id":"cc616748-2b5f-4756-b9d2-4324b1ef92d4","resolution":{"observed_at":"2026-08-07T01:05:26.835657Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-08-06T18:28:29.688777Z","title":"Think only when you need with large hybrid-reasoning models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08297","last_updated":"2025-07-21T10:37:40Z","snapshot_observed_at":"2026-08-07T21:25:45.428255Z","submitted_at":"2025-07-11T04:07:10Z","title":"KAT-V1: Kwai-AutoThink Technical Report","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T18:28:29.688777Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2507.08297"},"observation_digest":"sha256:7b3c7c9bb613175c091d0c25aa2dc0dde3810bbb717105e94e79a52f6349f80f","observation_id":"ee207456-fbe3-4f58-ae61-6c7c64b52dd9","resolution":{"observed_at":"2026-08-06T18:28:29.688777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-08-06T17:53:55.825220Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.09662","last_updated":"2025-07-13T14:51:59Z","snapshot_observed_at":"2026-08-07T01:15:50.475193Z","submitted_at":"2025-07-13T14:51:59Z","title":"Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-08-06T17:53:55.825220Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2507.09662"},"observation_digest":"sha256:222e6e8777a9b93208c737a48c4e721c15fa4bc47aef1e6b46bd870d737ffaa8","observation_id":"deeda259-d89e-4528-877a-206597c2a1cf","resolution":{"observed_at":"2026-08-06T17:53:55.825220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":"2505.14631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2505.14631 , year=","venue":null,"work_id":"4cd57d2e-121e-4e2f-a03f-2f7de71d5ab6","year":2025},"citing_paper":{"arxiv_id":"2507.14958","last_updated":"2026-05-24T11:58:35Z","snapshot_observed_at":"2026-08-06T15:41:37.418672Z","submitted_at":"2025-07-20T13:36:19Z","title":"MUR: Momentum Uncertainty guided Reasoning for Large Language Models","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-19T03:34:06.058415Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2507.14958"},"observation_digest":"sha256:7975963e50bbc0b37b414d637c6a95496d977f34ee7e617d2d1337ccd3c179d4","observation_id":"b175c513-4c90-447c-a019-188be8758e66","resolution":{"observed_at":"2026-05-19T03:37:01.259623Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":"2505.14631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2505.14631 , year=","venue":null,"work_id":"4cd57d2e-121e-4e2f-a03f-2f7de71d5ab6","year":2025},"citing_paper":{"arxiv_id":"2507.15586","last_updated":"2026-04-20T10:32:25Z","snapshot_observed_at":"2026-08-07T09:22:09.876613Z","submitted_at":"2025-07-21T13:03:55Z","title":"Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation","version":7},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-19T04:14:01.829209Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2507.15586"},"observation_digest":"sha256:6c6049c6083f38a0ce533ae99fdcfb8ab2585653ecca31dc14d0abfb42e2c2f6","observation_id":"8fbc63a1-844d-42c5-b21d-2f4f8e4f0de4","resolution":{"observed_at":"2026-05-19T04:17:03.364841Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-08-06T15:31:28.102020Z","title":"URL https:// doi.org/10.48550/arXiv.2505.14631","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.15844","last_updated":"2025-08-07T14:12:39Z","snapshot_observed_at":"2026-08-08T23:54:30.672141Z","submitted_at":"2025-07-21T17:52:34Z","title":"Hierarchical Budget Policy Optimization for Adaptive Reasoning","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T15:31:28.102020Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2507.15844"},"observation_digest":"sha256:a8d997207e43d1c8b2f6419af125257a266785ba5167cf009433259ba40ad120","observation_id":"e0714b9d-f4a4-46c2-9aee-ba6de4f8bcca","resolution":{"observed_at":"2026-08-06T15:31:28.102020Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-08-06T13:54:26.778546Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.20000","last_updated":"2025-07-26T16:40:17Z","snapshot_observed_at":"2026-08-06T13:54:23.524199Z","submitted_at":"2025-07-26T16:40:17Z","title":"Matching Game Preferences Through Dialogical Large Language Models: A Perspective","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T13:54:26.778546Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2507.20000"},"observation_digest":"sha256:eba6315be99cdbcb300f31036992f45f0781b377c2595f69f1940b85f1276bec","observation_id":"e746a584-e3c8-451f-9faa-cd3802a6abca","resolution":{"observed_at":"2026-08-06T13:54:26.778546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-08-04T16:07:31.525481Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.16679","last_updated":"2025-09-20T13:11:28Z","snapshot_observed_at":"2026-08-04T16:07:24.699834Z","submitted_at":"2025-09-20T13:11:28Z","title":"Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-04T16:07:31.525481Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2509.16679"},"observation_digest":"sha256:7665b4dfbee43a3b84d6dd1f954d3f96da1e16ab4a8cc7c76b1ba24e78baa98d","observation_id":"11a487ad-e7a2-4edf-88fa-750f4e1bae50","resolution":{"observed_at":"2026-08-04T16:07:31.525481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-08-04T13:51:51.270386Z","title":"Think only when you need with large hybrid-reasoning models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.24560","last_updated":"2026-08-02T06:52:09Z","snapshot_observed_at":"2026-08-08T20:31:19.086455Z","submitted_at":"2025-09-29T10:13:55Z","title":"AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T13:51:51.270386Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2509.24560"},"observation_digest":"sha256:f013faddd03a1c88d80c2a835f2af024169c5ad0b095d668c27d46b3ad1c3c7b","observation_id":"8c09e99f-430d-41c8-82e7-a477716cc7bd","resolution":{"observed_at":"2026-08-04T13:51:51.270386Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":"2505.14631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2505.14631 , year=","venue":null,"work_id":"4cd57d2e-121e-4e2f-a03f-2f7de71d5ab6","year":2025},"citing_paper":{"arxiv_id":"2601.05300","last_updated":"2026-05-04T11:14:47Z","snapshot_observed_at":"2026-08-03T03:42:08.545849Z","submitted_at":"2026-01-08T13:24:49Z","title":"TIME: Temporally Intelligent Meta-reasoning Engine for Context-Triggered Explicit Reasoning","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-16T15:49:26.750021Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2601.05300"},"observation_digest":"sha256:dd17ac4ade8f85530e8c76b9bd18e5e52b915e0de039339b96e71ec42dc23b76","observation_id":"5ba7e694-be8b-4db0-980b-3a00d60f2403","resolution":{"observed_at":"2026-05-16T15:51:05.439841Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-08-03T05:43:53.786778Z","title":"Think only when you need with large hybrid-reasoning models.arXiv preprint arXiv:2505.14631, 2025a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.01472","last_updated":"2026-06-24T08:53:47Z","snapshot_observed_at":"2026-08-03T05:43:51.520535Z","submitted_at":"2026-02-01T22:31:19Z","title":"ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T05:43:53.786778Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2602.01472"},"observation_digest":"sha256:1aca20a2a4362416932772fd33099f88ff6b5a0fb6b8c7bdfa9f91c21c8c54b3","observation_id":"e0925b38-4263-4386-893c-7b67fdfbc585","resolution":{"observed_at":"2026-08-03T05:43:53.786778Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-07-13T23:28:12.790404Z","title":"Think only when you need with large hybrid-reasoning models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.16867","last_updated":"2026-06-03T09:37:20Z","snapshot_observed_at":"2026-08-06T21:47:33.826393Z","submitted_at":"2026-03-17T17:59:51Z","title":"Efficient Reasoning on the Edge","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-13T23:28:12.790404Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2603.16867"},"observation_digest":"sha256:430278339049a8aa3b96a5981b8bde8f95c4dc94ef8002b3d479be47043d6688","observation_id":"9f7b5512-257c-4ef5-bd9d-7048e409a4fc","resolution":{"observed_at":"2026-07-13T23:28:12.790404Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":"2505.14631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2505.14631 , year=","venue":null,"work_id":"4cd57d2e-121e-4e2f-a03f-2f7de71d5ab6","year":2025},"citing_paper":{"arxiv_id":"2604.08232","last_updated":"2026-04-09T13:22:24Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-09T13:22:24Z","title":"HiRO-Nav: Hybrid ReasOning Enables Efficient Embodied Navigation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-10T17:01:13.350219Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2604.08232"},"observation_digest":"sha256:a770941f36f6c915311a327219c87ad0bcdb3e552641bae79e041b289d8721d0","observation_id":"00073c2d-6880-4c3e-b99d-7a8d455b29fd","resolution":{"observed_at":"2026-05-11T07:41:01.533819Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":"2505.14631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2505.14631 , year=","venue":null,"work_id":"4cd57d2e-121e-4e2f-a03f-2f7de71d5ab6","year":2025},"citing_paper":{"arxiv_id":"2605.02913","last_updated":"2026-04-08T00:53:29Z","snapshot_observed_at":"2026-07-06T23:15:55.848885Z","submitted_at":"2026-04-08T00:53:29Z","title":"Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-05-10T19:15:27.406778Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2605.02913"},"observation_digest":"sha256:429d3955986e48076c90d2752dbf8cff3dee5893ca4f4989b36e13c5d8ff9345","observation_id":"d759670c-2a0f-4c74-8a18-ff337e9b1510","resolution":{"observed_at":"2026-05-10T23:15:48.850663Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":"2505.14631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2505.14631 , year=","venue":null,"work_id":"4cd57d2e-121e-4e2f-a03f-2f7de71d5ab6","year":2025},"citing_paper":{"arxiv_id":"2605.22138","last_updated":"2026-05-21T08:11:54Z","snapshot_observed_at":"2026-07-06T23:32:30.578213Z","submitted_at":"2026-05-21T08:11:54Z","title":"Efficient Agentic Reasoning Through Self-Regulated Simulative Planning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-22T06:33:36.846345Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2605.22138"},"observation_digest":"sha256:80b4921307217b5c8f60d440ba8d84c3ccb4e81c3386904e0c4ac9cacb669ddd","observation_id":"0fefb37d-7c46-4e3b-90ba-e917fb90336a","resolution":{"observed_at":"2026-05-22T06:34:41.000756Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"cited_work":{"arxiv_id":"2505.14631","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.14631","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2505.14631 , year=","venue":null,"work_id":"4cd57d2e-121e-4e2f-a03f-2f7de71d5ab6","year":2025},"citing_paper":{"arxiv_id":"2605.22873","last_updated":"2026-05-20T03:15:46Z","snapshot_observed_at":"2026-08-01T10:03:51.429507Z","submitted_at":"2026-05-20T03:15:46Z","title":"When Do LLMs Reason? A Dynamical Systems View via Entropy Phase Transitions","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-25T06:14:49.430548Z"},"links":{"cited_paper":"/paper/2505.14631","citing_paper":"/paper/2605.22873"},"observation_digest":"sha256:791d7c3c9df2577b19a83c8a1e286308bbd4c1baf45f632409b0c67342ed75bf","observation_id":"b4dc2f30-c3a2-43f4-9a57-c6458cde1fa5","resolution":{"observed_at":"2026-05-25T06:15:23.473176Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.14631/citation-record","integrity":"/paper/2505.14631/integrity","json":"/paper/2505.14631/citation-record.json","paper":"/paper/2505.14631"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T15:37:12.794855Z","title":"Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs","venue":null,"work_id":"1e3fc91c-a4c6-4662-b6f4-d5b2825792cf","year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:02.180730Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:56f7af40b75ea8b7360e6b8d97a920941ee5ca9d5841c8bd1ed4576ef69f201a","observation_id":"f0d258e0-2497-4a57-ab5a-d0cb54663bce","resolution":{"observed_at":"2026-08-07T15:37:12.893144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T15:37:02.243996Z","title":"Aime 2024, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:02.243996Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:2fb366d5ed958e8603a9b9cb55120dccd8d5cb264a5992d11350fbc470ec9c20","observation_id":"16d6e4c3-1b4f-49a3-871e-49c12aa119cb","resolution":{"observed_at":"2026-08-07T15:37:02.243996Z","resolver_source":null,"status":"parse_uncertain"},"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":"2026-08-07T15:37:12.565763Z","title":"Claude 3.7 sonnet and claude code","venue":null,"work_id":"77c22b81-1577-48f5-9361-90e060391c5a","year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:02.352656Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:9a8dd5cad481e6afac4a0de8d837084b60c7681482f134ba62375aba886e929c","observation_id":"7339d900-16c0-4e30-a86b-a880b24cbc53","resolution":{"observed_at":"2026-08-07T15:37:12.670595Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T15:37:02.474112Z","title":"Program synthesis with large language models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:02.474112Z"},"links":{"cited_paper":"/paper/2108.07732","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:4017bb927a5019749d6d0ee57178defbab0b0dc7fa6899fb62fb036a9f9d75f5","observation_id":"2749490d-dcd0-46c4-8f29-7f8eab4048ed","resolution":{"observed_at":"2026-08-07T15:37:02.474112Z","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":"2026-08-07T15:37:12.294782Z","title":"Le, Christopher Ré, and Azalia Mirhoseini","venue":null,"work_id":"454d8f6e-ad7d-4a8b-bfff-5879e6915f4c","year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:02.602243Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:8d75e1490a078f6819ed52e445ddb8920c4238b29f810780d88d62965eda6492","observation_id":"d10e9891-aca8-488f-be3f-d2e7216e192a","resolution":{"observed_at":"2026-08-07T15:37:12.418759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T15:37:12.060369Z","title":"Kcts: Knowledge-constrained tree search decoding with token-level hallucination detection, 2023","venue":null,"work_id":"237e6898-51af-42d3-bbe1-749d27a3d3ea","year":2023},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:02.723740Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:aaa9a0c7e0a09ebb4e74674206a8f7224e50857cc2e9a6488dd4bbd339ebf43e","observation_id":"3d66c9a4-7d13-44b5-9abc-b3511e2ff18c","resolution":{"observed_at":"2026-08-07T15:37:12.156197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T15:37:02.820708Z","title":"R1-v: Reinforcing super generalization ability in vision-language models with less than \\ 3","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:02.820708Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:baa55d572f2a3d6d6707bfa8d6e896694de26d02c797c80c724291941cdc6b25","observation_id":"bc61526b-c2b1-4815-9b98-fd9221a49629","resolution":{"observed_at":"2026-08-07T15:37:02.820708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.21187","last_updated":"2025-02-01T07:57:37Z","snapshot_observed_at":"2026-08-01T16:43:44.704797Z","submitted_at":"2024-12-30T18:55:12Z","title":"Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.21187","snapshot_observed_at":"2026-08-07T15:37:02.982109Z","title":"Do not think that much for 2+ 3=? on the overthinking of o1-like llms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:02.982109Z"},"links":{"cited_paper":"/paper/2412.21187","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:0326ec671c4ef16ae724a662e85cc8fc0d59559d57e601ebd5503a6b9ebe987a","observation_id":"cbc6923d-6884-4494-9691-cc77d2758eaa","resolution":{"observed_at":"2026-08-07T15:37:02.982109Z","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":"2026-08-07T15:37:03.084426Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:03.084426Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:4b1723120ac5a8380f4bb8d682ddd3efce81342c6a35f8b389c333ce51cd7e17","observation_id":"bfefc874-29c1-462c-962b-82f402d608d2","resolution":{"observed_at":"2026-08-07T15:37:03.084426Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.04475","last_updated":"2025-03-10T09:27:03Z","snapshot_observed_at":"2026-07-06T17:56:23.317089Z","submitted_at":"2024-04-06T02:29:02Z","title":"Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.04475","snapshot_observed_at":"2026-08-07T15:37:03.194688Z","title":"Length-controlled alpacaeval: A simple way to debias automatic evaluators","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:03.194688Z"},"links":{"cited_paper":"/paper/2404.04475","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:6baf7757488c80352b9e36f1c9104caa85b8005649373cc01c18e5ae740179d2","observation_id":"49e5e1f7-ff45-47d4-a941-253c91ad1d54","resolution":{"observed_at":"2026-08-07T15:37:03.194688Z","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":"2026-08-07T15:37:03.308859Z","title":"Open r1: A fully open reproduction of deepseek-r1, January 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:03.308859Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:1e68b8c345cbcf9db945fe845bb97d479bae6b5d5226b7457bdfbfe4a652dd79","observation_id":"574c23be-a34a-4f9a-9237-11a0993ea8b6","resolution":{"observed_at":"2026-08-07T15:37:03.308859Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.21776","last_updated":"2025-10-22T16:42:24Z","snapshot_observed_at":"2026-08-05T07:15:29.998948Z","submitted_at":"2025-03-27T17:59:51Z","title":"Video-R1: Reinforcing Video Reasoning in MLLMs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.21776","snapshot_observed_at":"2026-08-07T15:37:03.477338Z","title":"Video-r1: Reinforcing video reasoning in mllms","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:03.477338Z"},"links":{"cited_paper":"/paper/2503.21776","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:b87d776041b3ffbcc80543c35287ff9de22946ad02df8612eb80db1a087f567b","observation_id":"576b7f90-f7fe-4b8d-86c2-b46c553ce4a0","resolution":{"observed_at":"2026-08-07T15:37:03.477338Z","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":"2026-08-07T15:37:11.788740Z","title":"Gemini 2.5 flash","venue":null,"work_id":"9068d094-616b-4664-8d89-603f1dcaf18b","year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:03.631168Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:b2cca9e5d0d0be612fbb4fedfdd9fd40c2e72e381eeb8fce553beae1842f542e","observation_id":"5a0a6d19-3472-43b9-b256-9bd39e8d0040","resolution":{"observed_at":"2026-08-07T15:37:11.928092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-07T15:37:03.740911Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:03.740911Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:c686484d24e9c3bc9fe29ff06ca9060dc21419819772fe5ce0dd39ed99468c47","observation_id":"828d9768-a2e2-45a2-bcde-c74dffe2e801","resolution":{"observed_at":"2026-08-07T15:37:03.740911Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.06749","last_updated":"2026-02-28T21:10:52Z","snapshot_observed_at":"2026-08-07T18:44:26.813869Z","submitted_at":"2025-03-09T20:06:45Z","title":"Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.06749","snapshot_observed_at":"2026-08-07T15:37:03.889816Z","title":"Vision-r1: Incentivizing reasoning capability in multimodal large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:03.889816Z"},"links":{"cited_paper":"/paper/2503.06749","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:5048dbbd85d7e35ba5a9decfaec06f9805906ed04898b988f1c11617089a8919","observation_id":"35d7d021-0049-4a9e-91e5-aeb30c502b1f","resolution":{"observed_at":"2026-08-07T15:37:03.889816Z","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":"2026-08-07T15:37:03.993628Z","title":"Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:03.993628Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:0d3847314569269ed4ea720b354b5e036de81c817853502529672c951997f612","observation_id":"c71ef2fb-77fc-42ee-9c0b-f1a2f3602df9","resolution":{"observed_at":"2026-08-07T15:37:03.993628Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.03262","last_updated":"2025-11-10T15:11:13Z","snapshot_observed_at":"2026-08-02T05:27:47.490711Z","submitted_at":"2025-01-04T02:08:06Z","title":"REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.03262","snapshot_observed_at":"2026-08-07T15:37:04.130902Z","title":"Reinforce++: A simple and efficient approach for aligning large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:04.130902Z"},"links":{"cited_paper":"/paper/2501.03262","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:ca450fb06cf918b370444ca7ea75710e981b2f9db9c5a72cb0cad5353b5f8066","observation_id":"71bb26c6-2df2-42da-9920-c64c728256cd","resolution":{"observed_at":"2026-08-07T15:37:04.130902Z","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":"2026-08-07T15:37:11.533317Z","title":"Rewarding chatbots for real-world engagement with millions of users, 2023","venue":null,"work_id":"2918babd-1c16-4c19-a12d-c1480d98afb6","year":2023},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:04.244585Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:56521d37d101af230b508e0d18e2409ad49948ca598817e3d3db8a53f455a90d","observation_id":"f4e50156-ea22-4684-b6f8-2050133d8fd0","resolution":{"observed_at":"2026-08-07T15:37:11.652175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1612.03651","last_updated":"2016-12-12T12:51:03Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2016-12-12T12:51:03Z","title":"FastText.zip: Compressing text classification models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.03651","snapshot_observed_at":"2026-08-07T15:37:04.391801Z","title":"Fasttext.zip: Compressing text classification models","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:04.391801Z"},"links":{"cited_paper":"/paper/1612.03651","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:6824b412ebbe4d9e8b16e087125faca94a3e2cd5ba5d1bdb224af038744c18c0","observation_id":"92fce269-ba75-46b3-9b09-3b33a9d0ca99","resolution":{"observed_at":"2026-08-07T15:37:04.391801Z","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":"2026-08-10T03:07:35.408836Z","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":"2026-08-07T15:37:04.484693Z","title":"Livecodebench: Holistic and contamination free evaluation of large language models for code","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:04.484693Z"},"links":{"cited_paper":"/paper/2403.07974","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:5ee6fd39cb9e5cd7c6512a5ae3a455538894e7a0a8bc97a8001b1bd62f851d72","observation_id":"445c261e-a4aa-4db5-9e8e-0af7b39bb338","resolution":{"observed_at":"2026-08-07T15:37:04.484693Z","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":"2026-08-07T15:37:11.283240Z","title":"o pf, Yannic Kilcher, Dimitri Von R \\","venue":null,"work_id":"808217b8-276b-4aa6-8527-170e22912de5","year":2023},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:04.637500Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:d734a7ae5b8eb8b5aa91d7f294d76ce3cf6528ab5a88a6aa14468139b08aa2e3","observation_id":"73afd0e7-392d-4e94-876d-761860fe5d30","resolution":{"observed_at":"2026-08-07T15:37:11.375604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11939","last_updated":"2024-10-14T18:11:58Z","snapshot_observed_at":"2026-08-02T11:33:41.297984Z","submitted_at":"2024-06-17T17:26:10Z","title":"From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11939","snapshot_observed_at":"2026-08-07T15:37:04.744210Z","title":"From crowdsourced data to high-quality benchmarks: Arena-hard and benchbuilder pipeline","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:04.744210Z"},"links":{"cited_paper":"/paper/2406.11939","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:85d2633ce4b047cbd0a135aa25c83963ba94d603a77134289b90eb11e8560678","observation_id":"e3225f1e-9f0b-40d6-80a5-97ad8d566f60","resolution":{"observed_at":"2026-08-07T15:37:04.744210Z","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":"2026-08-07T15:37:11.162254Z","title":"Don't throw away your value model! generating more preferable text with value-guided monte-carlo tree search decoding, 2024","venue":null,"work_id":"1a51ad10-8935-49f6-8550-86ecf272a64e","year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:04.869021Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:9d339741b815a97535693f919dcb43032b634795e5fade7c66a3d1222986b59d","observation_id":"52bdb838-558a-4c5e-9a22-d2380370e451","resolution":{"observed_at":"2026-08-07T15:37:11.198576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T15:37:05.028806Z","title":"A simple model of inference scaling laws, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:05.028806Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:e758dc2ededa5bca8a8d26ed7ba9525b47f72105547a2be5053f02f3dd0591bd","observation_id":"0c077f6d-67ad-43d5-bb8a-c968717ef7be","resolution":{"observed_at":"2026-08-07T15:37:05.028806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.20050","last_updated":"2023-05-31T17:24:00Z","snapshot_observed_at":"2026-08-05T13:11:04.104454Z","submitted_at":"2023-05-31T17:24:00Z","title":"Let's Verify Step by Step","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.20050","snapshot_observed_at":"2026-08-07T15:37:05.168215Z","title":"Let's verify step by step","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:05.168215Z"},"links":{"cited_paper":"/paper/2305.20050","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:ef10e4f896549175acb2e691a0dadd14be3efc079e1bb4d13cfd38183823dccd","observation_id":"f7534cc9-8241-4aa0-ae45-6aaf9d149475","resolution":{"observed_at":"2026-08-07T15:37:05.168215Z","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":"2026-08-07T15:37:10.928015Z","title":"Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, Yuling Gu, Saumya Malik, Victoria Graf, Jena D","venue":null,"work_id":"56ecf7fb-6296-4dda-a826-fb34daf0ea7f","year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:05.316252Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:3da71697cb16ad22aa8bf96e2b098abc008013f82194c2abd8a293f3eff467b0","observation_id":"d849a0e2-fa79-4d83-ad47-402e1a9f7dea","resolution":{"observed_at":"2026-08-07T15:37:11.081533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T15:37:05.462460Z","title":"Deepscaler: Surpassing o1-preview with a 1.5b model by scaling rl, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:05.462460Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:64db80ca39ed7bd384a6723a14875bcfb2d6d5d076f0e535aa4d4237ad464619","observation_id":"f1bc9e7d-950c-42b6-b786-68d5824ea077","resolution":{"observed_at":"2026-08-07T15:37:05.462460Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.18942","last_updated":"2025-04-01T06:52:58Z","snapshot_observed_at":"2026-08-07T16:40:27.846388Z","submitted_at":"2025-03-24T17:59:04Z","title":"Video-T1: Test-Time Scaling for Video Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.18942","snapshot_observed_at":"2026-08-07T15:37:05.609652Z","title":"Video-t1: Test-time scaling for video generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:05.609652Z"},"links":{"cited_paper":"/paper/2503.18942","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:df9f96a8cb6b2cb7e311e3665c276df9975d57d6090fe0be4e7a26be5311bdaa","observation_id":"c48f9134-afa7-41dd-8531-81feb49ec946","resolution":{"observed_at":"2026-08-07T15:37:05.609652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.06450","last_updated":"2024-08-12T18:59:13Z","snapshot_observed_at":"2026-08-07T08:27:28.863794Z","submitted_at":"2024-08-12T18:59:13Z","title":"Evaluating Language Models for Efficient Code Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.06450","snapshot_observed_at":"2026-08-07T15:37:05.745275Z","title":"Evaluating language models for efficient code generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:05.745275Z"},"links":{"cited_paper":"/paper/2408.06450","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:fab4e1f9e833542bc90e5f3982c277f3757308c0675ae1981d421a60aeb850c7","observation_id":"d98699e6-9a86-48e7-a4ad-1c808cfa95d3","resolution":{"observed_at":"2026-08-07T15:37:05.745275Z","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":"2026-08-07T15:37:10.658585Z","title":"Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation","venue":null,"work_id":"9083fb7b-d18d-4e3e-91a0-e5ae77e86c5b","year":2023},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:05.885968Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:3a043d4eb4e0b9d536e6a6dc82d91f4255a40a97c0339ac593c077a3d038c421","observation_id":"1e152a90-affe-4a9e-a99d-ec3c89bff1e9","resolution":{"observed_at":"2026-08-07T15:37:10.771774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-07T15:37:06.016932Z","title":"Aimo-2 winning solution: Building state-of-the-art mathematical reasoning models with openmathreasoning dataset","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:06.016932Z"},"links":{"cited_paper":"/paper/2504.16891","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:e8dfb096a8e7ba5c4bd5a66fabe8b4212d7b7b7332aab21e18bf696b5b12e323","observation_id":"b0c159a4-e73f-446e-a49e-152e5bc61f69","resolution":{"observed_at":"2026-08-07T15:37:06.016932Z","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":"2026-08-07T15:37:10.419105Z","title":"Synthetic-1: Two million collaboratively generated reasoning traces from deepseek-r1, 2025","venue":null,"work_id":"6c811c9a-fd1a-49a8-b552-6827c024e5e7","year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:06.160760Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:896c3cb87250bcdb57da69e6e4fb0fec16217ee7ea91ca7243e06c8693b0ca63","observation_id":"e5f95b16-9cae-4c54-8e70-64a4e58f0faf","resolution":{"observed_at":"2026-08-07T15:37:10.529054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.14275","last_updated":"2025-06-27T02:05:51Z","snapshot_observed_at":"2026-08-10T15:12:46.563738Z","submitted_at":"2025-01-24T06:39:38Z","title":"Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.14275","snapshot_observed_at":"2026-08-07T15:37:06.270809Z","title":"Leveraging online olympiad-level math problems for llms training and contamination-resistant evaluation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:06.270809Z"},"links":{"cited_paper":"/paper/2501.14275","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:b8d06237263c00db4e57efda9cd3e10caa0c896c137c30f5b3f4627e063ef25c","observation_id":"2a1259fe-f11a-455b-9049-eb0266b843d1","resolution":{"observed_at":"2026-08-07T15:37:06.270809Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.19393","last_updated":"2025-03-01T06:07:39Z","snapshot_observed_at":"2026-07-06T20:29:11.710285Z","submitted_at":"2025-01-31T18:48:08Z","title":"s1: Simple test-time scaling","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.19393","snapshot_observed_at":"2026-08-07T15:37:06.396049Z","title":"s1: Simple test-time scaling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:06.396049Z"},"links":{"cited_paper":"/paper/2501.19393","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:bd0ef145b685cada027e7aa013422ce59c652251974755929fd83ac5ec8809b4","observation_id":"3407b5e5-f555-4074-a656-535baa5b0ffc","resolution":{"observed_at":"2026-08-07T15:37:06.396049Z","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":"2026-08-07T15:37:10.173927Z","title":"Openai gpt-4.5 system card","venue":null,"work_id":"349a4b70-2fa2-4d5f-afb3-10836eee7734","year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:06.556468Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:b7d0184f7870f7b4aedb2a490daea61d8aa2057de98ef76c8b2d1ef16ffbc8fc","observation_id":"db651975-7325-4fee-8d08-61b9c3d2d262","resolution":{"observed_at":"2026-08-07T15:37:10.303703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T15:37:06.669215Z","title":"Codeforces","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:06.669215Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:82b079de4b037375896d77c13e42d27c5a0cd8dbc8db42a4daa3e35a3a7838b1","observation_id":"1f51961e-61a5-416b-8683-f1b618632f5e","resolution":{"observed_at":"2026-08-07T15:37:06.669215Z","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":"2026-08-07T15:37:06.783845Z","title":"Direct preference optimization: Your language model is secretly a reward model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:06.783845Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:d7406cf4bbfad830ee2ac9c0539fe8aed57f785bea0247de92c2c3c4d2bcf534","observation_id":"b7533716-0534-4be9-87c0-ffd0695bd9a7","resolution":{"observed_at":"2026-08-07T15:37:06.783845Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.16419","last_updated":"2025-08-21T19:14:40Z","snapshot_observed_at":"2026-08-07T04:27:23.738927Z","submitted_at":"2025-03-20T17:59:38Z","title":"Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.16419","snapshot_observed_at":"2026-08-07T15:37:06.927043Z","title":"Stop overthinking: A survey on efficient reasoning for large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:06.927043Z"},"links":{"cited_paper":"/paper/2503.16419","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:ff9b1675c2822ca071ecc55d9f01311465c654225fa701f4cfc786f93d7efc2f","observation_id":"6dd2c238-f4b8-4d16-9d22-c442ad2fd0ec","resolution":{"observed_at":"2026-08-07T15:37:06.927043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03314","last_updated":"2024-08-06T17:35:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-06T17:35:05Z","title":"Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03314","snapshot_observed_at":"2026-08-07T15:37:07.046362Z","title":"Scaling llm test-time compute optimally can be more effective than scaling model parameters","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:07.046362Z"},"links":{"cited_paper":"/paper/2408.03314","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:8ce69fc4a71c55617fe9412c2a28496b95283defb08664545362b8152f87806b","observation_id":"8057ca5a-8f4c-4009-af6b-087baae7ef50","resolution":{"observed_at":"2026-08-07T15:37:07.046362Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-08-06T14:58:42.911363Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-07T15:37:07.201561Z","title":"Deepseekmath: Pushing the limits of mathematical reasoning in open language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:07.201561Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:60c156df5ad40d5cd83fe83f0364e613d4318a11663d4136843ac4aa7af026a7","observation_id":"81b2bf7a-c4d3-46e2-bdcd-e7794f6878d1","resolution":{"observed_at":"2026-08-07T15:37:07.201561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.19256","last_updated":"2024-10-02T04:01:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-28T06:20:03Z","title":"HybridFlow: A Flexible and Efficient RLHF Framework","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.19256","snapshot_observed_at":"2026-08-07T15:37:07.281547Z","title":"Hybridflow: A flexible and efficient rlhf framework","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:07.281547Z"},"links":{"cited_paper":"/paper/2409.19256","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:553ca8b93875a53eefc8514b820674a5544b1eb7e6d4ab2981f78b25fd43c74b","observation_id":"25224b00-7549-49f9-be9f-1ee1602fc7f7","resolution":{"observed_at":"2026-08-07T15:37:07.281547Z","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":"2026-08-07T15:37:07.425645Z","title":"Qwq: Reflect deeply on the boundaries of the unknown, November 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:07.425645Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:84c6c720968bc32ddcabd700629a0d6ed5899c93f0ac9a0df534bf3245a55090","observation_id":"3da7e1f6-03a9-4a55-81b7-6676fba6cf84","resolution":{"observed_at":"2026-08-07T15:37:07.425645Z","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":"2026-08-07T15:37:07.549965Z","title":"Open Thoughts","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:07.549965Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:fc7b2f18b4fec8c111701045138f4e8a6c054574e51a2c0de9e5a7c2fbc4f2af","observation_id":"c651c1ea-e9f7-4746-9a6f-cb553169bdde","resolution":{"observed_at":"2026-08-07T15:37:07.549965Z","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":"2026-08-07T15:37:07.667246Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:07.667246Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:054048f5d9015df71297957d72601d1b2db94077b1c1b41c584bf75652529ced","observation_id":"83313ddc-5ae2-49d2-a188-d8f26fdcdd85","resolution":{"observed_at":"2026-08-07T15:37:07.667246Z","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":"2026-08-07T15:37:07.813994Z","title":"Teaching language models to critique via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:07.813994Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:2ced3f8e406f1916b06ea2b1a78a210c721bb3cbcf4ace99b5ef2615c7197bb4","observation_id":"27a3f907-0e81-4c9a-8714-5be174e0e325","resolution":{"observed_at":"2026-08-07T15:37:07.813994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.09686","last_updated":"2025-01-23T08:44:44Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T17:37:58Z","title":"Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.09686","snapshot_observed_at":"2026-08-07T15:37:07.954014Z","title":"Towards large reasoning models: A survey of reinforced reasoning with large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:07.954014Z"},"links":{"cited_paper":"/paper/2501.09686","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:6cad17261d742855b46af0a90b1a751baab9131182c485af7193712f03fa8eb5","observation_id":"0890e113-2653-46f3-b9ae-affb29002967","resolution":{"observed_at":"2026-08-07T15:37:07.954014Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-07T15:37:08.071421Z","title":"Kodcode: A diverse, challenging, and verifiable synthetic dataset for coding, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:08.071421Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:cb92e9ae230b5e3d83f83d1de37a11d20ca9830c98674dceb8af1747d8a11e0a","observation_id":"1b54a3d3-1f3d-41ea-bcd8-e461a2f45e5f","resolution":{"observed_at":"2026-08-07T15:37:08.071421Z","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":"2026-08-07T15:37:08.235606Z","title":"Limo: Less is more for reasoning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:08.235606Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:4cd9144378a31dc7c371c524f7884d9d702ed558e01b968eb61feeb6d7fc77b9","observation_id":"cc2a5f7e-7a87-416e-935b-345a2b7b87a8","resolution":{"observed_at":"2026-08-07T15:37:08.235606Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.01825","last_updated":"2023-09-13T03:57:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-08-03T15:34:01Z","title":"Scaling Relationship on Learning Mathematical Reasoning with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.01825","snapshot_observed_at":"2026-08-07T15:37:08.367668Z","title":"Scaling relationship on learning mathematical reasoning with large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:08.367668Z"},"links":{"cited_paper":"/paper/2308.01825","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:84c6d4ce699903372c7ec215ac81ceda207dc8946f73d253bf1e517c9737e409","observation_id":"0bae6449-bd68-4026-bb9b-cde7fffb9001","resolution":{"observed_at":"2026-08-07T15:37:08.367668Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-07T15:37:08.519460Z","title":"Qwen2.5 technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:08.519460Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:dd2137fe19dc151ea99161bdd4d0ddb0648d2f867673a38b69d9790778f390d3","observation_id":"ea9a1f93-1b18-4fe7-bfe4-4b4cef12a26f","resolution":{"observed_at":"2026-08-07T15:37:08.519460Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12122","last_updated":"2024-09-18T16:45:37Z","snapshot_observed_at":"2026-07-06T19:17:41.512834Z","submitted_at":"2024-09-18T16:45:37Z","title":"Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12122","snapshot_observed_at":"2026-08-07T15:37:08.612688Z","title":"Qwen2.5-math technical report: Toward mathematical expert model via self-improvement","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:08.612688Z"},"links":{"cited_paper":"/paper/2409.12122","citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:4e5eec62a50c87b422e59e3ea97d5ccb1415511917d3b3049cec198af57b4326","observation_id":"717989c8-9e17-404a-86d0-ecbca2861eaa","resolution":{"observed_at":"2026-08-07T15:37:08.612688Z","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":"2026-08-07T15:37:08.717669Z","title":"Tenenbaum, and Chuang Gan","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:08.717669Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:c7767109a078e6e90eab4f9de42b0e6efc0de6d5670334bbed4dc45254f51a27","observation_id":"4f4cc201-e3e8-48d5-827f-537c122ee877","resolution":{"observed_at":"2026-08-07T15:37:08.717669Z","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":"2026-08-07T15:37:09.938770Z","title":"Wildchat: 1m chat GPT interaction logs in the wild","venue":null,"work_id":"9a2cedd3-0aae-48e6-83ac-a45bb3bdd814","year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:08.859847Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:96e44661b3e8d5b84904a4dbc0171877e626ebd4a0bf6030f5ae07494c0cd2d2","observation_id":"f9516010-d52e-479f-b09b-b2df2c742cf0","resolution":{"observed_at":"2026-08-07T15:37:10.070000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T15:37:09.692734Z","title":"1.4 million open-source distilled reasoning dataset to empower large language model training, 2025","venue":null,"work_id":"6876db0a-d5a7-4b74-a60a-73c0e9abedc5","year":2025},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:08.969345Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:070ff0dade1be5dd3d83055e2ea82e05a78ab116cf39c1d4328a23b0860efea1","observation_id":"046f9701-2421-4d16-8269-2c0146556b93","resolution":{"observed_at":"2026-08-07T15:37:09.810091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T15:37:09.098245Z","title":"Language agent tree search unifies reasoning acting and planning in language models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:09.098245Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:4bd12e33ee656d238a6167b0395ef2ad0a228a5c32278c4c56ecab9c510084ca","observation_id":"ac801e08-10be-4f01-8df2-481f0e76f39a","resolution":{"observed_at":"2026-08-07T15:37:09.098245Z","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":"2026-08-07T15:37:09.232525Z","title":"Llamafactory: Unified efficient fine-tuning of 100+ language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models","version":2},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-07T15:37:09.232525Z"},"links":{"citing_paper":"/paper/2505.14631"},"observation_digest":"sha256:6de2760d065ae5da8855224c96bad421b3b478bd56528dc628ea01c76be8a963","observation_id":"7075ccf1-0a07-480b-8316-aaba5b244fde","resolution":{"observed_at":"2026-08-07T15:37:09.232525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.14631","last_updated":"2025-05-21T05:17:34Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-10T02:03:47.948620Z","submitted_at":"2025-05-20T17:23:25Z","title":"Think Only When You Need with Large Hybrid-Reasoning Models"},"reference_resolution":{"displayed":56,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":40,"verified_exact":0,"verified_fuzzy":14},"total_outbound_references":56},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 17 inbound Pith citation observations for arXiv:2505.14631."}