{"as_of":"2026-07-15T05:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:927bae7b1c1895022c4b5ca9cad9c08278cb0abf31ebe9f27a56e3d0c7642f8c","coverage":[{"denominator":42,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":42,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T11:51:01.769882Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-07-14T06:31:01.685423+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2605.28421/citation-record","integrity":"/paper/2605.28421/integrity","json":"/paper/2605.28421/citation-record.json","paper":"/paper/2605.28421"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.14740","last_updated":"2024-02-26T18:26:25Z","snapshot_observed_at":"2026-07-06T17:34:07.737296Z","submitted_at":"2024-02-22T17:52:34Z","title":"Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs","version":2},"cited_work":{"arxiv_id":"2402.14740","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.14740","snapshot_observed_at":"2026-07-09T08:56:06.435604Z","title":"Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs","venue":"cs.LG","work_id":"7bb8f9ec-1241-4472-a4fa-c636c6d79892","year":2024},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2402.14740","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:6ab1ede6e16522e2eeb9365f437f7bc4aa9569632d120fa101a4eab8e76fb83c","observation_id":"37e14206-a075-475f-ae9a-460573103097","resolution":{"observed_at":"2026-06-29T11:53:23.681551Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:669dc6080acddd330560b80e5f3325fddc391969a7afcc8d100ec7c5f5296b41","observation_id":"bec034d0-20ff-47f8-a0c3-62ebb9ab9c50","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:915a3c74cd941fc0c785f903e35bc8e2b3ece967c7c7374c7481d7c89f793d94","observation_id":"f6b7c844-a0e7-4c8f-95e7-faa5ce9ee977","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.09390","last_updated":"2023-12-14T23:07:33Z","snapshot_observed_at":"2026-07-06T17:02:09.539730Z","submitted_at":"2023-12-14T23:07:33Z","title":"Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision","version":1},"cited_work":{"arxiv_id":"2312.09390","doi":"10.48550/arxiv.2312.09390","metadata_source":"pith","pith_arxiv_id":"2312.09390","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Burns, P","venue":"cs.CL","work_id":"4a21c761-9a3d-4f84-a55d-5779da5da28f","year":2023},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2312.09390","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:db05aa272b176b3f15adaa636e25b997f043d332601cc7377c91a8bd7dfc82a5","observation_id":"a0e04240-5782-4147-90e3-08a2d48c1c32","resolution":{"observed_at":"2026-06-29T11:53:23.662416Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-13T23:49:45.539419+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T23:49:45.539419+00:00","source":"openalex_status_cache"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.08191","doi":"10.48550/arxiv.2510.08191","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Training-free group relative policy optimization, October 2025","venue":null,"work_id":"e147d6d6-f3f4-44ff-94e9-78f84be63bdb","year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:22d7d27758300dff3269dd085301acd31c9aa1c41e5344d372d42eb1d44c93e0","observation_id":"d092d25e-94da-4b7a-bcdd-e913d5d9d672","resolution":{"observed_at":"2026-06-29T11:53:23.684505Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2510.26277","doi":"10.48550/arxiv.2510.26277","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Do LLMs signal when they’re right? evidence from neuron agreement, 2025","venue":null,"work_id":"c6738122-c6cc-4da9-b098-338e12efde09","year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:6463e43946fef098a3e6fa5776424992d48ebbfff8bdeb9d8aae527222f06056","observation_id":"617b1317-457d-444b-afd8-1775a422db2b","resolution":{"observed_at":"2026-06-29T11:53:23.669549Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2605.05742","last_updated":"2026-05-07T06:33:25Z","snapshot_observed_at":"2026-07-06T23:18:17.333660Z","submitted_at":"2026-05-07T06:33:25Z","title":"Weak-to-Strong Generalization is Nearly Inevitable (in Linear Models)","version":1},"cited_work":{"arxiv_id":"2605.05742","doi":null,"metadata_source":"pith","pith_arxiv_id":"2605.05742","snapshot_observed_at":"2026-06-29T11:53:23.700060Z","title":"Weak-to-Strong Generalization is Nearly Inevitable (in Linear Models)","venue":"cs.LG","work_id":"1060ca8b-10d9-477c-88a4-e57b18922da4","year":2026},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2605.05742","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:ef4365b825df3b4ac5b3eab1b2441b9a7cc2f6a858dde706a837386f66cf64e8","observation_id":"c3af9b88-12f8-4561-a053-5c6a4dbb51d7","resolution":{"observed_at":"2026-06-29T11:53:23.701503Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+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":"2501.12948","doi":"10.1016/j.artmed.2024.103001","metadata_source":"pith","pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","venue":"cs.CL","work_id":"e6b75ad5-2877-4168-97c8-710407094d20","year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:b3be33909884ec6f513e57d122ddacc1cd555164ee0ed2fb93a855212160c1a0","observation_id":"ee8bc249-2756-4881-9ac8-131095b16c32","resolution":{"observed_at":"2026-06-29T11:53:23.704929Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11456","last_updated":"2025-05-22T19:12:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-15T17:59:51Z","title":"DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning","version":2},"cited_work":{"arxiv_id":"2504.11456","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.11456","snapshot_observed_at":"2026-07-09T03:25:57.840663Z","title":"DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning","venue":"cs.CL","work_id":"3dea79f1-73da-4db2-8d7b-64013c3c5fa5","year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2504.11456","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:5bb01d247246a7ebd47f4e917c9824a98e69ac3c903a0e87fd9ade3027badbec","observation_id":"9bee5b38-0f96-4678-a3e3-9faac621eb5c","resolution":{"observed_at":"2026-06-29T11:53:23.707364Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.03874","last_updated":"2021-11-08T21:30:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-03-05T18:59:39Z","title":"Measuring Mathematical Problem Solving With the MATH Dataset","version":2},"cited_work":{"arxiv_id":"2103.03874","doi":"10.48550/arxiv.2103.03874","metadata_source":"pith","pith_arxiv_id":"2103.03874","snapshot_observed_at":"2026-07-10T16:57:24.565388Z","title":"Measuring Mathematical Problem Solving With the MATH Dataset","venue":"cs.LG","work_id":"50652ac6-fb7c-4675-a2c2-159c241feb17","year":2021},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2103.03874","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:79c36fc2c0464ca622a638f746eafe1f2bc714755cbe731001aedbb833b5d713","observation_id":"2a5b6baa-ef87-4718-b6af-dd058bf0a6b3","resolution":{"observed_at":"2026-06-29T11:53:23.709943Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T18:20:22.649941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T18:20:22.649941+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T18:20:22.649941+00:00","source":"openalex_status_cache"},{"observed_at":"2026-07-14T06:30:57.218914+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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:c061679361bc6ae0f8de1c56d493820a167ab5b7bc1c85a29f3ba03e96bdc975","observation_id":"02abb5a9-9cce-43d8-9681-20c7560d5002","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:7d941cea10a005e4ee0fef84451409420d9e6019ab5d30a2f33633384d7230ec","observation_id":"4ee058bb-acd1-484c-a80e-5b5a31989c8c","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","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":"2504.16828","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-04T20:40:07.881388Z","title":"Process reward models that think","venue":null,"work_id":"5eb327be-4cef-462e-8ccc-1fafa6d30e28","year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:5224ae20d0dcfd19b6e31bae52892fcc429c51f0abeece8b5fc3c6d06d0761b0","observation_id":"1993e84a-6c8b-4fca-9cc6-f0781a897f26","resolution":{"observed_at":"2026-06-29T11:53:23.715281Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2510.00705","last_updated":"2026-06-26T15:22:20Z","snapshot_observed_at":"2026-07-06T22:31:20.110402Z","submitted_at":"2025-10-01T09:20:51Z","title":"Training-free Uncertainty Guidance for Complex Visual Tasks with MLLMs","version":3},"cited_work":{"arxiv_id":"2510.00705","doi":null,"metadata_source":"pith","pith_arxiv_id":"2510.00705","snapshot_observed_at":"2026-06-29T11:53:23.711158Z","title":"Training-free uncertainty guidance for complex visual tasks with mllms.arXiv preprint arXiv:2510.00705","venue":"cs.CV","work_id":"27e7ae02-8293-4c18-a0bd-802824b7e5bf","year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2510.00705","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:d0d51efc2620c2ae7f77b341bbf3753477a7978e65d30efab45ee7f923efff1a","observation_id":"d0ec393c-dffe-4757-af64-54b9d339b4d5","resolution":{"observed_at":"2026-06-29T11:53:23.712612Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2511.14166","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T11:53:23.688562Z","title":null,"venue":null,"work_id":"bf86aaf2-fdfc-4815-9ffd-01f85d9cdfdf","year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:c4610517592d581f6194ef42b0e72f98bddb7384ead11bd96ac64eb52b4d889b","observation_id":"2d59e6bc-6b99-4350-8fdc-a5e4edce4d91","resolution":{"observed_at":"2026-06-29T11:53:23.689895Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2601.22718","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T04:47:37.872104Z","title":"arXiv preprint arXiv:2601.22718 , year=","venue":null,"work_id":"c3544b5b-2386-42a5-ae9e-d86e155af07e","year":2026},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:481b34085c2c8b630127c63be72ff079f75998f39f1190a24205dcb0e2708c34","observation_id":"9149339f-63a5-4e9d-92b1-e79fcab7d694","resolution":{"observed_at":"2026-06-29T11:53:23.686496Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:7f3a9a229b5db796186e06e44d4cfafbb82366f0954b0c73f76d69f082d217cc","observation_id":"fcdeebe8-0569-4bde-89ea-8cb96c50e3ea","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:c91ee6853f53377dd9fd9f549bcec912f23e73fcd9682839e2af5fb62687594b","observation_id":"cacb1f9c-d9b2-4846-8426-53f881f94825","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.07809","last_updated":"2026-04-20T09:03:51Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-08-11T09:49:01Z","title":"EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning","version":5},"cited_work":{"arxiv_id":"2508.07809","doi":null,"metadata_source":"pith","pith_arxiv_id":"2508.07809","snapshot_observed_at":"2026-06-29T11:53:23.697384Z","title":"EvoCoT: Overcoming the Exploration Bottleneck in Reinforcement Learning","venue":"cs.LG","work_id":"878d25a1-8a74-4746-a55a-66d0645ce895","year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2508.07809","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:0571684c75b85f5ef789c454c08dc062cefdb07ca54eb6afebbf10a95986d91b","observation_id":"c682f812-c459-439d-8c41-b594f9ddd14a","resolution":{"observed_at":"2026-06-29T11:53:23.698614Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.19641","last_updated":"2025-06-04T05:08:08Z","snapshot_observed_at":"2026-07-06T21:30:23.899598Z","submitted_at":"2025-05-26T07:59:36Z","title":"SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond","version":4},"cited_work":{"arxiv_id":"2505.19641","doi":"10.48550/arxiv.2505.19641","metadata_source":"arxiv_reference","pith_arxiv_id":"2505.19641","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Synlogic: Synthesizing verifiable reasoning data at scale for learning logical reasoning and beyond","venue":null,"work_id":"11a55d46-fb86-4226-b0f0-2144857aeb9c","year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2505.19641","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:7040decb5ddd36a33ad8b3b6825ced62e1d1eb40059665b73c924fd2eca86f34","observation_id":"09397fe9-541f-4b71-a434-e42ba757d11a","resolution":{"observed_at":"2026-06-29T11:53:23.704451Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:2258436138f9b09e68f8b78eaa54afad2ddfa94bfca9a79c4ce3259ad47bcad1","observation_id":"802b7182-ffa2-446d-8122-166375d722d5","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:ae397c101977eec2cce73989eaca78f6bef8de18653a2847cd32835b8d0c4ceb","observation_id":"c0dd154d-c180-490e-8dfa-1389a00e51c1","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","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":"2601.18779","doi":"10.48550/arxiv.2601.18779","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Pope: Learning to reason on hard problems via privileged on-policy exploration","venue":null,"work_id":"49d6a516-37ca-4bd7-8174-20b4b69db931","year":2026},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:c815d0c0966d8a770fca72879afe97aa09f16957482bd2a7960b019313bcf915","observation_id":"7c49f55d-bd89-413c-8cdb-09ad2e4a4af6","resolution":{"observed_at":"2026-06-29T11:53:23.698713Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":"1707.06347","doi":"10.1016/j.artint.2010.12.005","metadata_source":"pith","pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Proximal Policy Optimization Algorithms","venue":"cs.LG","work_id":"240c67fe-d14d-4520-91c1-38a4e272ca19","year":2017},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:b4a1c5f2b3a28dc7c9cb441935b8094c5f1d77180af34edff5a141ffae35b630","observation_id":"d918def2-29c6-4970-9c8b-d88b4fe8c921","resolution":{"observed_at":"2026-06-29T11:53:23.684012Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2601.18795","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T03:25:57.801171Z","title":"Reuse your flops: Scaling rl on hard problems by conditioning on very off-policy prefixes","venue":null,"work_id":"2be8a44f-c445-4fc6-9eec-82c26f802afc","year":2026},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:00ae309a22cf9fc30062ffa53baba96f1541f166de33cd8df8e1dfb983043d9b","observation_id":"fb0250b3-8437-417d-ac4a-7d68a45c6eef","resolution":{"observed_at":"2026-06-29T11:53:23.689537Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","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":"2402.03300","doi":"10.1016/0004-3702(73)90011-8","metadata_source":"pith","pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","venue":"cs.CL","work_id":"c5006563-f3ec-438a-9e35-b7b484f34828","year":2024},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:e0e9760e8916780b9fcfc4490eda808e74d5dd8a7bf8b7a223c86751892de753","observation_id":"0bab13af-6ac6-42bc-af36-8a382921f3b6","resolution":{"observed_at":"2026-06-29T11:53:23.652281Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:d4cbd05de5de37937321bb569872faf53adec92863d13085f11e6008ee7f1d32","observation_id":"049b37ea-e7c2-45e3-be6c-5938937028bb","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:eed417743e08e04cb61e8a8100442b95fb11e98be10de2f295aefcacf20c2c76","observation_id":"cdc7c0b7-22be-446d-bcd0-3d83e69cbc0d","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:858e9fcdcbf2225e2a074ec1ffbf46797f746f0ed0b3b9bd0ce35e3cb603f30b","observation_id":"a1de8982-be7b-40bf-8bce-f93b70684c22","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":"2505.09388","doi":"10.1016/j.aiopen.2022.12","metadata_source":"pith","pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Qwen3 Technical Report","venue":"cs.CL","work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e","year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:29b805946ddec668f43d0d38484de07d924103347c458258b985956c1d17b1fb","observation_id":"60c4c9ce-e9fa-40da-a4af-6a89c7a9703a","resolution":{"observed_at":"2026-06-29T11:53:23.672403Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:59dce8d7e29c09aeaffd30216a78e1c68a04d76c1076e07f18cac2647d103590","observation_id":"f46631b7-1aec-4f9d-aaec-10b5d9e639b6","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.00053","last_updated":"2022-10-31T18:09:51Z","snapshot_observed_at":"2026-07-06T14:12:45.730577Z","submitted_at":"2022-10-31T18:09:51Z","title":"Generating Sequences by Learning to Self-Correct","version":1},"cited_work":{"arxiv_id":"2211.00053","doi":"10.48550/arxiv.2211.00053","metadata_source":"arxiv_reference","pith_arxiv_id":"2211.00053","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Generating sequences by learning to self-correct","venue":null,"work_id":"ccd1e168-905a-48c2-a387-c43d5c25f8bc","year":2022},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2211.00053","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:203f3339444c9b581f6603ac44904b8be10969aceb5f6eadcee2c7c5a285365a","observation_id":"7d992c4d-6284-4b5b-b08d-3b4105e8a474","resolution":{"observed_at":"2026-06-29T11:53:23.676256Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.02864","last_updated":"2025-06-03T13:28:57Z","snapshot_observed_at":"2026-07-06T21:35:51.635385Z","submitted_at":"2025-06-03T13:28:57Z","title":"BNPO: Beta Normalization Policy Optimization","version":1},"cited_work":{"arxiv_id":"2506.02864","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.02864","snapshot_observed_at":"2026-07-04T08:09:40.713683Z","title":"arXiv preprint arXiv:2506.02864 , year=","venue":null,"work_id":"14ce32a8-5db9-49f8-b890-9695ba59f1ee","year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2506.02864","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:08b96700e305f0e62a732a580b2d247c718578d7883b156c6190078b28aa73fa","observation_id":"64639df8-7838-4fb5-8962-e9ea3bb461ae","resolution":{"observed_at":"2026-06-29T11:53:23.678846Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.04809","last_updated":"2026-05-04T13:55:24Z","snapshot_observed_at":"2026-07-06T22:41:05.793337Z","submitted_at":"2026-01-08T10:42:04Z","title":"SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning","version":5},"cited_work":{"arxiv_id":"2601.04809","doi":null,"metadata_source":"pith","pith_arxiv_id":"2601.04809","snapshot_observed_at":"2026-07-04T17:09:59.118536Z","title":"SCALER:Synthetic Scalable Adaptive Learning Environment for Reasoning","venue":"cs.AI","work_id":"b64419eb-b675-430f-82c8-ba9b335004f6","year":2026},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2601.04809","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:1efd1e5bdb9ffb4fd34c03a79f2ef4208bc5d8dff08b470c772097753846f344","observation_id":"dd996933-3890-41ae-9507-12d77df4d16f","resolution":{"observed_at":"2026-06-29T11:53:23.667020Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:118c5d56ed7825bf3cc6ba55ea3d94b888df6ff3faf50c194fc9438aca25930f","observation_id":"0d33a8be-bd11-4930-a06d-c1b7dc0a01b1","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10671","last_updated":"2024-09-10T13:25:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-07-15T12:35:42Z","title":"Qwen2 Technical Report","version":4},"cited_work":{"arxiv_id":"2407.10671","doi":"10.18653/v1/2024.naacl-long.246","metadata_source":"pith","pith_arxiv_id":"2407.10671","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Qwen2 Technical Report","venue":"cs.CL","work_id":"a1857881-ab9b-4b80-9b5f-9ae4b5c2566d","year":2024},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2407.10671","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:23efcdcc636f0740d12266bf31f67f6bae6241ecbc8d37348a1d8b13033be1ea","observation_id":"34a259c7-bc2a-4585-8edb-a0efa7bb0702","resolution":{"observed_at":"2026-06-29T11:53:23.676192Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:01d1bab39d8b9827ac83de2b84621789fda034c6086fef50a6475278bc0af67f","observation_id":"b351e235-5754-48d3-b6f4-adda2fbbee5a","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.12627","last_updated":"2026-04-14T11:53:23Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-14T11:53:23Z","title":"KnowRL: Boosting LLM Reasoning via Reinforcement Learning with Minimal-Sufficient Knowledge Guidance","version":1},"cited_work":{"arxiv_id":"2604.12627","doi":null,"metadata_source":"pith","pith_arxiv_id":"2604.12627","snapshot_observed_at":"2026-06-29T11:53:23.663504Z","title":"KnowRL: Boosting LLM Reasoning via Reinforcement Learning with Minimal-Sufficient Knowledge Guidance","venue":"cs.AI","work_id":"16597dd3-6f3e-4c93-a68b-57af9f092bef","year":2026},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2604.12627","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:0f6a4c371c8dad7fa860d480a6d8f3d1a0604d022254abd608a3dec3c687cc9a","observation_id":"e0abe62f-3451-4811-b91e-5eb208bee888","resolution":{"observed_at":"2026-06-29T11:53:23.664636Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+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":"2026-06-29T11:51:01.769882Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:4fb117bb1243b100a8ff75f0678c3f84653fd6bfcbf4bec6a5f1b9f7fb0b0d66","observation_id":"e14d4db1-9649-4d22-a45d-e35d04018528","resolution":{"observed_at":"2026-06-29T11:51:01.769882Z","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":"2505.20072","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-07T12:33:45.213471Z","title":"Incentivizing strong reasoning from weak supervision.arXiv preprint arXiv:2505.20072, 2025","venue":null,"work_id":"e780f076-a52c-4c89-944a-16272b73ed95","year":2025},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:ce875ff1f8e3d98657f60b1f64a839eb512e7c619d34dc0718e42d3ce0999836","observation_id":"5faf7014-cab4-4e1e-8d0c-92bbb22d437f","resolution":{"observed_at":"2026-06-29T11:53:23.669676Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2508.05592","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T11:53:23.677594Z","title":"Mathsmith: Towards extremely hard mathematical reasoning by forging synthetic problems with a reinforced policy","venue":null,"work_id":"413bcd50-7b9d-4ad9-be19-a66aa2cca9fb","year":2026},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:a9fcdc885e97e3d9a4dc9b9f01b7ff6b3801c0ba9a0f463f582ac40fa7d216e5","observation_id":"570f1cdf-1701-4847-b222-8dd6d85b8c23","resolution":{"observed_at":"2026-06-29T11:53:23.679110Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2602.22495","last_updated":"2026-06-17T23:32:06Z","snapshot_observed_at":"2026-07-06T22:47:06.893212Z","submitted_at":"2026-02-26T00:20:39Z","title":"Reinforcement-aware Knowledge Distillation for LLM Reasoning","version":3},"cited_work":{"arxiv_id":"2602.22495","doi":null,"metadata_source":"pith","pith_arxiv_id":"2602.22495","snapshot_observed_at":"2026-07-07T16:24:01.056987Z","title":"Reinforcement-aware Knowledge Distillation for LLM Reasoning","venue":"cs.LG","work_id":"e1b3cffd-f5ae-4eb9-abb7-6df5fa58497e","year":2026},"citing_paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-29T11:51:01.769882Z"},"links":{"cited_paper":"/paper/2602.22495","citing_paper":"/paper/2605.28421"},"observation_digest":"sha256:5748777f4fac4f4c4176d6d2b54bdb9ca9e5322f0f7e9f35e0bee452d07e7b13","observation_id":"a06f4f99-00cc-44dc-a733-1b7c78396f7c","resolution":{"observed_at":"2026-06-29T11:53:23.660779Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-14T06:31:01.685423+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.28421","last_updated":"2026-05-27T12:52:58Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-06T23:37:59.285884Z","submitted_at":"2026-05-27T12:52:58Z","title":"DenoiseRL: Bootstrapping Reasoning Models to Recover from Noisy Prefixes"},"reference_resolution":{"displayed":42,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":27,"verified_fuzzy":0},"total_outbound_references":42},"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":"2026-07-14T06:31:01.685423+00:00","source":"crossref"},{"observed_at":"2026-07-14T06:30:57.218914+00:00","source":"retraction_watch"}],"thesis":"As of 15 July 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2605.28421."}