{"as_of":"2026-08-17T18:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2a1a45f789bc441b4b20b5c23c7fcfb1c960aeff59ebdb2cfaba51b9e3d86d2e","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":31,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:36:29.130196Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-10T18:17:33.884574Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":"2402.10176","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-07-10T18:17:33.884574Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset.arXiv preprint arXiv: Arxiv-2402.10176","venue":"cs.CL","work_id":"9000f710-e68c-43ea-854e-4e984f57f380","year":2024},"citing_paper":{"arxiv_id":"2406.18629","last_updated":"2024-06-26T17:43:06Z","snapshot_observed_at":"2026-08-06T00:24:52.274888Z","submitted_at":"2024-06-26T17:43:06Z","title":"Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-18T23:58:29.040819Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2406.18629"},"observation_digest":"sha256:24df3d304fde91325d923e7ab6d9dee56d0c9fc0e5a343e96b41bcdbed7025b2","observation_id":"d17fe6da-b6cc-4608-972f-a4cf8b53a4e7","resolution":{"observed_at":"2026-05-18T23:58:29.190696Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-12T19:33:00.973681Z","title":"Openmathinstruct- 1: A 1.8 million math instruction tuning dataset","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10640","last_updated":"2024-11-16T00:14:51Z","snapshot_observed_at":"2026-08-16T03:42:56.050856Z","submitted_at":"2024-11-16T00:14:51Z","title":"BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices","version":1},"reference_index":117,"source":"pdf_text","source_observed_at":"2026-08-12T19:33:00.973681Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2411.10640"},"observation_digest":"sha256:a638b2b87594390f99c4cc63a1015e7bf388eba12d3c7a24c09daf43ee96837c","observation_id":"c59e5fd0-f2fc-4845-bb61-65e06aeb48b8","resolution":{"observed_at":"2026-08-12T19:33:00.973681Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-11T22:57:02.025496Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02980","last_updated":"2024-12-09T22:23:41Z","snapshot_observed_at":"2026-08-15T13:18:49.671776Z","submitted_at":"2024-12-04T02:47:45Z","title":"Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models","version":2},"reference_index":191,"source":"arxiv_source","source_observed_at":"2026-08-11T22:57:02.025496Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2412.02980"},"observation_digest":"sha256:29cd5a157e9cf99a0a9cee29ef03d60390d323371dc6158f2741ae66275855d9","observation_id":"b3454ee7-84c5-4843-a610-8b5660d27b35","resolution":{"observed_at":"2026-08-11T22:57:02.025496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-11T14:44:12.855418Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.11699","last_updated":"2024-12-16T12:21:11Z","snapshot_observed_at":"2026-08-12T22:20:30.348369Z","submitted_at":"2024-12-16T12:21:11Z","title":"CoinMath: Harnessing the Power of Coding Instruction for Math LLMs","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T14:44:12.855418Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2412.11699"},"observation_digest":"sha256:4a2556853c48ea0e21fcf528895f24093b8561878bfa97273adbb2c713920ba3","observation_id":"756b4e2c-e938-48a9-b014-e399386d5f86","resolution":{"observed_at":"2026-08-11T14:44:12.855418Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-11T12:40:16.279886Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.15283","last_updated":"2024-12-18T16:07:44Z","snapshot_observed_at":"2026-08-15T15:00:21.498650Z","submitted_at":"2024-12-18T16:07:44Z","title":"Channel Merging: Preserving Specialization for Merged Experts","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-11T12:40:16.279886Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2412.15283"},"observation_digest":"sha256:d3964b4fc608869236033ac3dca5fbcc5044a65a672b52f3c8d694ffcdadb7ed","observation_id":"b476cf6b-f17f-4eec-89a1-83ada1013fa7","resolution":{"observed_at":"2026-08-11T12:40:16.279886Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-11T12:34:16.793493Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.15285","last_updated":"2024-12-18T18:41:18Z","snapshot_observed_at":"2026-08-17T11:43:06.332223Z","submitted_at":"2024-12-18T18:41:18Z","title":"Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-11T12:34:16.793493Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2412.15285"},"observation_digest":"sha256:e52174c4ab263a2eeecd58ba4358536ec04c6b793b42b9f76bee8ca4d7a26355","observation_id":"12e1e2ce-e8eb-4fe8-b7d5-72f1adaa0654","resolution":{"observed_at":"2026-08-11T12:34:16.793493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-11T05:17:55.893662Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.17743","last_updated":"2024-12-24T16:07:47Z","snapshot_observed_at":"2026-08-16T08:15:47.006654Z","submitted_at":"2024-12-23T17:47:53Z","title":"YuLan-Mini: An Open Data-efficient Language Model","version":2},"reference_index":101,"source":"arxiv_source","source_observed_at":"2026-08-11T05:17:55.893662Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2412.17743"},"observation_digest":"sha256:220b2fcf4b4b15fdd9b0690f86224d1799f3da455bcae09d51b69dfd02551c63","observation_id":"dcad2363-5df2-44b1-a0ac-0ecd4936744d","resolution":{"observed_at":"2026-08-11T05:17:55.893662Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-11T00:46:46.280584Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.19361","last_updated":"2024-12-26T22:04:23Z","snapshot_observed_at":"2026-08-17T18:00:27.064163Z","submitted_at":"2024-12-26T22:04:23Z","title":"Dynamic Skill Adaptation for Large Language Models","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-11T00:46:46.280584Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2412.19361"},"observation_digest":"sha256:2b160b3963f17c7c948a6539a7b37e334776ef716fd5371c47a9727d61536f5c","observation_id":"604bf68c-ce34-4a14-98d4-5241734a0f54","resolution":{"observed_at":"2026-08-11T00:46:46.280584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-10T18:04:34.762554Z","title":"Openmathinstruct-1: A 1.8 million math instruction tun- ing dataset","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.14818","last_updated":"2025-01-20T18:40:47Z","snapshot_observed_at":"2026-08-11T01:59:38.596539Z","submitted_at":"2025-01-20T18:40:47Z","title":"Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models","version":1},"reference_index":177,"source":"pdf_text","source_observed_at":"2026-08-10T18:04:34.762554Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2501.14818"},"observation_digest":"sha256:7b5e2c755a3375354051ef30c6194b666c6bc03af8bbe3992264f63fd29df9f5","observation_id":"f4698a69-e795-4470-8dea-8bbe427d9c4c","resolution":{"observed_at":"2026-08-10T18:04:34.762554Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-09T18:07:52.204631Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.00691","last_updated":"2025-07-18T07:40:22Z","snapshot_observed_at":"2026-08-15T17:11:13.822689Z","submitted_at":"2025-02-02T06:32:23Z","title":"To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization","version":4},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-09T18:07:52.204631Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2502.00691"},"observation_digest":"sha256:59118aee50705259c622f4f1974ecb24ba2709c8c6b81a887d45234266c4a6c8","observation_id":"ec35cf08-3246-490b-97a5-75cac39869bd","resolution":{"observed_at":"2026-08-09T18:07:52.204631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-07T23:19:36.740535Z","title":"arXiv preprint arXiv:2402.10176","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.08904","last_updated":"2025-02-27T01:49:15Z","snapshot_observed_at":"2026-08-15T08:49:39.089464Z","submitted_at":"2025-02-13T02:40:33Z","title":"MIH-TCCT: Mitigating Inconsistent Hallucinations in LLMs via Event-Driven Text-Code Cyclic Training","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T23:19:36.740535Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2502.08904"},"observation_digest":"sha256:4aa3f05dd730d33601b62ce4d708409c50644b7e62ea77176d4f5f3c3f9dc07b","observation_id":"aedcdeab-651d-4064-9ab3-98c7d6bd8c9a","resolution":{"observed_at":"2026-08-07T23:19:36.740535Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":"2402.10176","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-07-10T18:17:33.884574Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset.arXiv preprint arXiv: Arxiv-2402.10176","venue":"cs.CL","work_id":"9000f710-e68c-43ea-854e-4e984f57f380","year":2024},"citing_paper":{"arxiv_id":"2502.10248","last_updated":"2025-02-24T10:12:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-14T15:58:10Z","title":"Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model","version":3},"reference_index":146,"source":"arxiv_source","source_observed_at":"2026-05-19T08:02:23.002090Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2502.10248"},"observation_digest":"sha256:dfc4e7783b16b936b3e491ca89d541b0f83c69e9e9fca757d7d5ae4091b41311","observation_id":"a47aa1ff-fe0d-4720-b0a2-e2571d6b4ae0","resolution":{"observed_at":"2026-05-19T08:02:23.658012Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-16T04:36:29.130196Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.00903","last_updated":"2025-05-01T22:47:06Z","snapshot_observed_at":"2026-08-17T06:48:45.211394Z","submitted_at":"2025-05-01T22:47:06Z","title":"NeMo-Inspector: A Visualization Tool for LLM Generation Analysis","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-16T04:36:29.130196Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2505.00903"},"observation_digest":"sha256:dbcc8ddb61fb68bc8ef7d2c28798fb30aefbc3583016d1c910e2eadc3279cb7d","observation_id":"471f92e2-a3f0-46f5-be09-2bb963a0d80d","resolution":{"observed_at":"2026-08-16T04:36:29.130196Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-15T23:36:29.881826Z","title":"preprint arXiv:2402.10176 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.04388","last_updated":"2025-05-28T20:14:44Z","snapshot_observed_at":"2026-08-16T19:51:56.501783Z","submitted_at":"2025-05-07T13:13:14Z","title":"The Aloe Family Recipe for Open and Specialized Healthcare LLMs","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T23:36:29.881826Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2505.04388"},"observation_digest":"sha256:6c28d92cde7790b0e3188860d05c6fedd48747f92e8628ae698b0865ac382c31","observation_id":"5e2c220c-bebc-4cfb-9fcd-32ab18d0c45a","resolution":{"observed_at":"2026-08-15T23:36:29.881826Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":"2402.10176","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-07-10T18:17:33.884574Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset.arXiv preprint arXiv: Arxiv-2402.10176","venue":"cs.CL","work_id":"9000f710-e68c-43ea-854e-4e984f57f380","year":2024},"citing_paper":{"arxiv_id":"2505.07527","last_updated":"2026-04-21T19:04:07Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-12T13:09:49Z","title":"Kalman Filter Enhanced GRPO for Reinforcement Learning-Based Language Model Reasoning","version":5},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-22T15:49:44.263123Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2505.07527"},"observation_digest":"sha256:79614e8b59f7f023d161b4c01f892178e2e2ea972d0e15662612b8b7fca86823","observation_id":"f2e1eab8-a5dc-448a-9fcc-ecfb27978475","resolution":{"observed_at":"2026-05-22T15:51:45.642038Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-15T20:21:21.566759Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.13307","last_updated":"2025-05-19T16:25:55Z","snapshot_observed_at":"2026-08-16T01:20:04.725929Z","submitted_at":"2025-05-19T16:25:55Z","title":"RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-15T20:21:21.566759Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2505.13307"},"observation_digest":"sha256:50bc056cf7c784f79d2c7b8d9335377d6b1875a35a50fc255f3fed1d68b359e8","observation_id":"dac958e1-052f-40cb-b0e9-838211590ea5","resolution":{"observed_at":"2026-08-15T20:21:21.566759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-07T15:29:09.451527Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15000","last_updated":"2025-05-21T01:07:00Z","snapshot_observed_at":"2026-08-14T02:16:34.541286Z","submitted_at":"2025-05-21T01:07:00Z","title":"Towards Spoken Mathematical Reasoning: Benchmarking Speech-based Models over Multi-faceted Math Problems","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T15:29:09.451527Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2505.15000"},"observation_digest":"sha256:aa284b7229fa357f02c9bac0798903a77b7365ae6316e08c7d6fe51ffa4f493c","observation_id":"1dfd7160-01e1-4910-accc-69f467d5916d","resolution":{"observed_at":"2026-08-07T15:29:09.451527Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-07T00:24:14.942013Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset.arXiv preprint arXiv:2402.10176, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.14234","last_updated":"2025-06-17T06:47:19Z","snapshot_observed_at":"2026-08-17T11:39:06.443806Z","submitted_at":"2025-06-17T06:47:19Z","title":"Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:14.942013Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2506.14234"},"observation_digest":"sha256:048c1d2a5fe9648cfdf645bf5b29d2b350a797f4768e6a7dcdee2666581cb844","observation_id":"a5f8ebd3-369d-4967-8041-1895d8c2e60d","resolution":{"observed_at":"2026-08-07T00:24:14.942013Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-15T19:40:08.889506Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.15455","last_updated":"2025-06-18T13:35:47Z","snapshot_observed_at":"2026-08-15T19:32:07.244546Z","submitted_at":"2025-06-18T13:35:47Z","title":"RE-IMAGINE: Symbolic Benchmark Synthesis for Reasoning Evaluation","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-15T19:40:08.889506Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2506.15455"},"observation_digest":"sha256:302cdc3a13c9e243057a111ad6a50c1ca90eacaa01c3c77c9b4719540ae59f81","observation_id":"0f75384e-28e4-4293-a4f9-85957ff60ff1","resolution":{"observed_at":"2026-08-15T19:40:08.889506Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-06T19:08:51.294515Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06427","last_updated":"2025-07-08T22:17:52Z","snapshot_observed_at":"2026-08-15T22:57:43.374995Z","submitted_at":"2025-07-08T22:17:52Z","title":"Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-06T19:08:51.294515Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2507.06427"},"observation_digest":"sha256:23995019284a1a735d7942c1edbb9ab0c5b0c6548a1326a181d017551292aea9","observation_id":"bbbbbc28-07f9-45d2-9a4e-30ca98fd30be","resolution":{"observed_at":"2026-08-06T19:08:51.294515Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-06T18:43:59.471326Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.07498","last_updated":"2025-07-14T07:10:51Z","snapshot_observed_at":"2026-08-15T12:03:04.307569Z","submitted_at":"2025-07-10T07:34:05Z","title":"Teaching LLM to Reason: Reinforcement Learning from Algorithmic Problems without Code","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-06T18:43:59.471326Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2507.07498"},"observation_digest":"sha256:57b2ccf88523080d8809e9d6c52a0c6d9bb4c24840d08bd871efa60add497688","observation_id":"ff4a753f-d84a-4aaf-b0b8-e6b537bc62dc","resolution":{"observed_at":"2026-08-06T18:43:59.471326Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-06T14:43:25.483084Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18013","last_updated":"2025-07-29T10:30:18Z","snapshot_observed_at":"2026-08-16T11:51:56.481241Z","submitted_at":"2025-07-24T01:00:48Z","title":"Technical Report of TeleChat2, TeleChat2.5 and T1","version":3},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-06T14:43:25.483084Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2507.18013"},"observation_digest":"sha256:6d36200ba33a3d5f3833caf8549eba1ba32dc3b07357461218ac30fcd99f587a","observation_id":"73b0262b-bf97-4d49-a9a8-fa423f51a54b","resolution":{"observed_at":"2026-08-06T14:43:25.483084Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-03T08:15:33.044123Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.17717","last_updated":"2026-06-09T20:25:14Z","snapshot_observed_at":"2026-08-17T03:28:47.079425Z","submitted_at":"2026-01-25T06:40:25Z","title":"A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data","version":3},"reference_index":214,"source":"arxiv_source","source_observed_at":"2026-08-03T08:15:33.044123Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2601.17717"},"observation_digest":"sha256:74f640e5e17020080dfa082a5043bf2c855e68fc0258ff0a5d3285d0b78a9eb1","observation_id":"38461d5c-a308-43d1-b89d-ea810d884c93","resolution":{"observed_at":"2026-08-03T08:15:33.044123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":"2402.10176","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-07-10T18:17:33.884574Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset.arXiv preprint arXiv: Arxiv-2402.10176","venue":"cs.CL","work_id":"9000f710-e68c-43ea-854e-4e984f57f380","year":2024},"citing_paper":{"arxiv_id":"2604.15529","last_updated":"2026-05-11T04:41:40Z","snapshot_observed_at":"2026-08-16T09:43:34.597574Z","submitted_at":"2026-04-16T21:19:35Z","title":"LACE: Lattice Attention for Cross-thread Exploration","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-10T10:24:16.283375Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2604.15529"},"observation_digest":"sha256:bfe1987221626551e84e082daa6fe7367121ae39828f174f17f75d304dd49fb6","observation_id":"6395c9b2-df9d-4c12-8b79-40a479676459","resolution":{"observed_at":"2026-05-10T10:24:21.260279Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":"2402.10176","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-07-10T18:17:33.884574Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset.arXiv preprint arXiv: Arxiv-2402.10176","venue":"cs.CL","work_id":"9000f710-e68c-43ea-854e-4e984f57f380","year":2024},"citing_paper":{"arxiv_id":"2604.15529","last_updated":"2026-05-11T04:41:40Z","snapshot_observed_at":"2026-08-16T09:43:34.597574Z","submitted_at":"2026-04-16T21:19:35Z","title":"LACE: Lattice Attention for Cross-thread Exploration","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-11T00:47:51.440441Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2604.15529"},"observation_digest":"sha256:ccc854827ae92c51a30084ed6b6f73ce6d5b2287d2a98ee35e7392cb4d39d3c9","observation_id":"16110f3f-cd12-4888-acc0-7863ab00cc96","resolution":{"observed_at":"2026-05-11T00:50:50.102667Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":"2402.10176","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-07-10T18:17:33.884574Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset.arXiv preprint arXiv: Arxiv-2402.10176","venue":"cs.CL","work_id":"9000f710-e68c-43ea-854e-4e984f57f380","year":2024},"citing_paper":{"arxiv_id":"2604.15529","last_updated":"2026-05-11T04:41:40Z","snapshot_observed_at":"2026-08-16T09:43:34.597574Z","submitted_at":"2026-04-16T21:19:35Z","title":"LACE: Lattice Attention for Cross-thread Exploration","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-12T04:05:35.063305Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2604.15529"},"observation_digest":"sha256:1df2d4fd9d6584c4edc50ff895e9f24e92c574d723537ebc9f32bd230ba179fc","observation_id":"d1575dc0-314a-4c1e-af5c-b1711e6ac1f9","resolution":{"observed_at":"2026-05-12T06:36:28.613113Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":"2402.10176","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-07-10T18:17:33.884574Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset.arXiv preprint arXiv: Arxiv-2402.10176","venue":"cs.CL","work_id":"9000f710-e68c-43ea-854e-4e984f57f380","year":2024},"citing_paper":{"arxiv_id":"2605.07711","last_updated":"2026-05-21T08:28:27Z","snapshot_observed_at":"2026-08-13T10:28:04.584789Z","submitted_at":"2026-05-08T13:16:17Z","title":"SimCT: Recovering Lost Supervision for Cross-Tokenizer On-Policy Distillation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-11T03:01:13.168529Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2605.07711"},"observation_digest":"sha256:55d7561d1200bd8719e8dc7887c0cd9a5a1005a295d31fcfd6013e77c46f6e6e","observation_id":"43baf9d1-cbe4-4f70-b60d-4c24ed52770f","resolution":{"observed_at":"2026-05-11T03:05:53.598587Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":"2402.10176","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-07-10T18:17:33.884574Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset.arXiv preprint arXiv: Arxiv-2402.10176","venue":"cs.CL","work_id":"9000f710-e68c-43ea-854e-4e984f57f380","year":2024},"citing_paper":{"arxiv_id":"2605.07711","last_updated":"2026-05-21T08:28:27Z","snapshot_observed_at":"2026-08-13T10:28:04.584789Z","submitted_at":"2026-05-08T13:16:17Z","title":"SimCT: Recovering Lost Supervision for Cross-Tokenizer On-Policy Distillation","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-22T10:30:11.309910Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2605.07711"},"observation_digest":"sha256:4e16231c0b8438437d7c6c15fc2934215f5bb2c0b86ea151d15203c0705885eb","observation_id":"00fa99fc-893c-4635-b27f-721c2ea0447d","resolution":{"observed_at":"2026-05-22T10:31:24.888136Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-07-11T13:53:36.775836Z","title":"arXiv preprint arXiv:2402.10176 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04763","last_updated":"2026-07-26T14:17:18Z","snapshot_observed_at":"2026-08-15T22:59:02.741838Z","submitted_at":"2026-07-06T07:56:53Z","title":"Multi-Turn On-Policy Distillation with Prefix Replay","version":1},"reference_index":79,"source":"arxiv_source","source_observed_at":"2026-07-11T13:53:36.775836Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2607.04763"},"observation_digest":"sha256:e51fe71b877695fdffdac2111f3882277b51864449e3e41ef259c5b24a6aa146","observation_id":"bb7a259c-8a56-495b-89fe-160b9a18435a","resolution":{"observed_at":"2026-07-11T13:53:36.775836Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-08-02T08:40:40.446065Z","title":"arXiv preprint arXiv:2402.10176 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04763","last_updated":"2026-07-26T14:17:18Z","snapshot_observed_at":"2026-08-15T22:59:02.741838Z","submitted_at":"2026-07-06T07:56:53Z","title":"Multi-Turn On-Policy Distillation with Prefix Replay","version":3},"reference_index":80,"source":"arxiv_source","source_observed_at":"2026-08-02T08:40:40.446065Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2607.04763"},"observation_digest":"sha256:31f7aa1fdc8e4a14ffd71868209e6ac6c655fed860d059e2f03e5d2d34aa326e","observation_id":"1e708e9c-c64e-4a44-9328-82b071028f5d","resolution":{"observed_at":"2026-08-02T08:40:40.446065Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset","version":2},"cited_work":{"arxiv_id":"2402.10176","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.10176","snapshot_observed_at":"2026-07-10T18:17:33.884574Z","title":"Openmathinstruct-1: A 1.8 million math instruction tuning dataset.arXiv preprint arXiv: Arxiv-2402.10176","venue":"cs.CL","work_id":"9000f710-e68c-43ea-854e-4e984f57f380","year":2024},"citing_paper":{"arxiv_id":"2607.07779","last_updated":"2026-07-08T17:46:36Z","snapshot_observed_at":"2026-07-12T23:17:59.879344Z","submitted_at":"2026-07-08T17:46:36Z","title":"From Solvers to Research: Large Language Model-Driven Formal Mathematics at the Research Frontier","version":1},"reference_index":225,"source":"pdf_text","source_observed_at":"2026-07-10T18:16:31.176239Z"},"links":{"cited_paper":"/paper/2402.10176","citing_paper":"/paper/2607.07779"},"observation_digest":"sha256:1ad39058cbf3b02f5078980ded978c62852d18fb8896605759feb077bd518aac","observation_id":"9e74047b-d01e-41fb-9376-0ea500c767f0","resolution":{"observed_at":"2026-07-10T18:17:33.885759Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.10176/citation-record","integrity":"/paper/2402.10176/integrity","json":"/paper/2402.10176/citation-record.json","paper":"/paper/2402.10176"},"outbound":[],"paper":{"arxiv_id":"2402.10176","last_updated":"2024-11-03T03:48:02Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T17:24:49.433939Z","submitted_at":"2024-02-15T18:26:11Z","title":"OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2402.10176."}