{"as_of":"2026-08-17T21:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:43dea11cc7969bb0b2de1eb93868b22171054f6d77d6aaf3ec564a330ec7797e","coverage":[{"denominator":88,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":88,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T21:17:14.346359Z","state":"measured"},{"denominator":91,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":91,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T19:40:08.816339Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-10T23:00:47.702466Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.04857","snapshot_observed_at":"2026-08-09T18:09:01.645942Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.01683","last_updated":"2025-02-02T06:36:01Z","snapshot_observed_at":"2026-08-15T18:51:10.225657Z","submitted_at":"2025-02-02T06:36:01Z","title":"LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-09T18:09:01.645942Z"},"links":{"cited_paper":"/paper/2412.04857","citing_paper":"/paper/2502.01683"},"observation_digest":"sha256:b4d84ee97d6de5ce3ae372018ec90679f59f48fc5f9c9307d09c4820a29f48e4","observation_id":"fa841f16-f458-4531-b321-e2c6e00cf99c","resolution":{"observed_at":"2026-08-09T18:09:01.645942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.04857","snapshot_observed_at":"2026-08-15T19:40:08.816339Z","title":"Neuro-symbolic data generation for math reasoning, 2024","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":25,"source":"arxiv_source","source_observed_at":"2026-08-15T19:40:08.816339Z"},"links":{"cited_paper":"/paper/2412.04857","citing_paper":"/paper/2506.15455"},"observation_digest":"sha256:b7fc1a54b2ea1aabb45f058be317039bad1d3f3219a052ab0d9e2821c69127ef","observation_id":"d1fc62b6-3321-49b9-9bba-357dee2558d7","resolution":{"observed_at":"2026-08-15T19:40:08.816339Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"cited_work":{"arxiv_id":"2412.04857","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.04857","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"1e83d48c-035c-4ea9-b748-eaf8f94c8d42","year":2024},"citing_paper":{"arxiv_id":"2604.09712","last_updated":"2026-04-08T06:28:03Z","snapshot_observed_at":"2026-08-15T03:27:56.745009Z","submitted_at":"2026-04-08T06:28:03Z","title":"LAST: Leveraging Tools as Hints to Enhance Spatial Reasoning for Multimodal Large Language Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-10T19:26:23.660206Z"},"links":{"cited_paper":"/paper/2412.04857","citing_paper":"/paper/2604.09712"},"observation_digest":"sha256:311033eb21dfc48cace5e33fed7e0d09c41fee3d44088cefc1fb268a6643bb7f","observation_id":"ebc1d33e-eb06-49a7-abf3-cf94bd7f7522","resolution":{"observed_at":"2026-05-10T23:00:47.705080Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2412.04857/citation-record","integrity":"/paper/2412.04857/integrity","json":"/paper/2412.04857/citation-record.json","paper":"/paper/2412.04857"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:12.794394Z","title":"Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, and Vedant Misra","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:12.794394Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:61e907608d4139eb2ae44f9d1c4a63df130544575ce3da91eb86eb9ddb47997c","observation_id":"3b20633a-c25b-43c5-be32-5bdd77146394","resolution":{"observed_at":"2026-08-11T21:17:12.794394Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:12.822260Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:12.822260Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:c0e77c1c0b1f3d87253bddda6bc791b6b778dd88cf7c7029692dbc69310f5e86","observation_id":"c4da938e-c257-433b-86d1-c85bf8324d7a","resolution":{"observed_at":"2026-08-11T21:17:12.822260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.18223","last_updated":"2026-03-18T05:34:39Z","snapshot_observed_at":"2026-08-14T10:40:26.323157Z","submitted_at":"2023-03-31T17:28:46Z","title":"A Survey of Large Language Models","version":19},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.18223","snapshot_observed_at":"2026-08-11T21:17:12.864759Z","title":"A survey of large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:12.864759Z"},"links":{"cited_paper":"/paper/2303.18223","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:802881002f3bd480dad74d774009a81217c2e17a20a6a14369eef867573a23a9","observation_id":"0f09e8c5-a7eb-4ce5-beaf-6a28f86e1c1d","resolution":{"observed_at":"2026-08-11T21:17:12.864759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.11432","last_updated":"2025-03-02T04:04:03Z","snapshot_observed_at":"2026-08-17T02:16:24.215789Z","submitted_at":"2023-08-22T13:30:37Z","title":"A Survey on Large Language Model based Autonomous Agents","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.11432","snapshot_observed_at":"2026-08-11T21:17:12.913133Z","title":"A survey on large language model based autonomous agents","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:12.913133Z"},"links":{"cited_paper":"/paper/2308.11432","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:9f22dc587e8770f01b808154b62df451d984358212d2a9612fe6dd2ff6e59295","observation_id":"28944a8b-4977-4ac4-b20b-6cada206b900","resolution":{"observed_at":"2026-08-11T21:17:12.913133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:12.964752Z","title":"Chatgpt for good? on opportunities and challenges of large language models for education","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:12.964752Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:a13f12370b36cf18141d6eeec94b0235c7af119645b2e02a06d819ecf44f0e24","observation_id":"2af9f3f3-468a-42dd-a2b7-09106c3492f3","resolution":{"observed_at":"2026-08-11T21:17:12.964752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:13.005662Z","title":"A survey on evaluation of large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.005662Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:d9a36ea00afb188b243944ff9488f31eaa22c4d956c80e85bff1081637b5f415","observation_id":"a53f30c8-3749-4eb0-94e1-3a89236cd8ee","resolution":{"observed_at":"2026-08-11T21:17:13.005662Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.10635","last_updated":"2024-06-28T08:24:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-20T07:01:57Z","title":"SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.10635","snapshot_observed_at":"2026-08-11T21:17:13.044874Z","title":"Scibench: Evaluating college-level scientific problem-solving abilities of large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.044874Z"},"links":{"cited_paper":"/paper/2307.10635","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:ea3e43d0e4576c5dd4b77289abe434e121bac1d106a391cbef99fba19163366c","observation_id":"1fb538c0-8acb-4313-b338-9188f7372ef6","resolution":{"observed_at":"2026-08-11T21:17:13.044874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13814","last_updated":"2023-02-28T02:06:53Z","snapshot_observed_at":"2026-08-16T15:53:12.157283Z","submitted_at":"2023-02-23T16:06:16Z","title":"An Independent Evaluation of ChatGPT on Mathematical Word Problems (MWP)","version":2},"cited_work":{"arxiv_id":"2302.13814","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.13814","snapshot_observed_at":"2026-08-11T21:17:14.963874Z","title":"An Independent Evaluation of ChatGPT on Mathematical Word Problems (MWP)","venue":"cs.CL","work_id":"a2289413-929e-44f1-8216-3c5a9306df33","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.084916Z"},"links":{"cited_paper":"/paper/2302.13814","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:7cc8c42d13128f440370e11c1edba98788aba5b89e105186738769a028907560","observation_id":"c444e9b8-2e2d-45f6-a7d1-b51a7c786dcd","resolution":{"observed_at":"2026-08-11T21:17:14.972563Z","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"}},{"citation":{"cited_paper":{"arxiv_id":"2309.17167","last_updated":"2024-03-14T09:52:16Z","snapshot_observed_at":"2026-08-16T14:56:32.958443Z","submitted_at":"2023-09-29T12:04:14Z","title":"DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.17167","snapshot_observed_at":"2026-08-11T21:17:13.124865Z","title":"Dyval: Graph-informed dynamic evaluation of large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.124865Z"},"links":{"cited_paper":"/paper/2309.17167","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:8ec1b1539972bde3c275f484fb481fa54a367f24e24729d5c57e555782330263","observation_id":"10e396b0-2f39-4d67-a49a-4e228e7af54b","resolution":{"observed_at":"2026-08-11T21:17:13.124865Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-14T02:43:01.480086Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-11T21:17:13.157009Z","title":"Training verifiers to solve math word problems","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.157009Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:d28f4378703212eb6a6612a5a7506c4eaaea96722ef93c1270bef2cda5b7e6bf","observation_id":"cd4d6c42-2119-481e-a0e2-1e12995f5832","resolution":{"observed_at":"2026-08-11T21:17:13.157009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:13.163074Z","title":"Measuring mathematical problem solving with the math dataset","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.163074Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:9ace9294cb7476d1cdb4e96100fc7d87b3f7f795d753e30d7f00e9f9d00adafd","observation_id":"1fb4bbf4-a3bc-49f7-941c-ad742a235b39","resolution":{"observed_at":"2026-08-11T21:17:13.163074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.07191","last_updated":"2021-04-15T06:11:12Z","snapshot_observed_at":"2026-08-07T13:11:17.300265Z","submitted_at":"2021-03-12T10:23:47Z","title":"Are NLP Models really able to Solve Simple Math Word Problems?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.07191","snapshot_observed_at":"2026-08-11T21:17:13.170174Z","title":"Are nlp models really able to solve simple math word problems? arXiv preprint arXiv:2103.07191, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.170174Z"},"links":{"cited_paper":"/paper/2103.07191","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:dd3610b738a9117f21896511b3433f005477e4ded7dad1c1e41be935430a4995","observation_id":"fc288421-7ff5-404b-a4d8-8bbeb9e495c7","resolution":{"observed_at":"2026-08-11T21:17:13.170174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.10631","last_updated":"2024-03-15T19:14:39Z","snapshot_observed_at":"2026-08-16T13:51:18.629241Z","submitted_at":"2023-10-16T17:54:07Z","title":"Llemma: An Open Language Model For Mathematics","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.10631","snapshot_observed_at":"2026-08-11T21:17:13.177653Z","title":"Llemma: An open language model for mathematics","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.177653Z"},"links":{"cited_paper":"/paper/2310.10631","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:992df136fc27e0ef6a97d839fff29e8dd58cb2da62bcaf0360229bb2fb5a16f9","observation_id":"8a270e69-32f8-4b9f-8ba3-fa26be4cbc75","resolution":{"observed_at":"2026-08-11T21:17:13.177653Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:13.186915Z","title":"Sampling constraint satisfaction solutions in the local lemma regime","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.186915Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:af5ab5c9fdb6b36d52c01c2de159c4f4612f7dcd8bf18abd630391ff372e67a6","observation_id":"3fad672f-8b87-44cd-a5b8-f31ee085b08e","resolution":{"observed_at":"2026-08-11T21:17:13.186915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:13.219024Z","title":"Softened symbol grounding for neuro-symbolic systems","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.219024Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:c5562437a56af8a5a31e633afb7ecff72dd56ccd2a5ccdf9494fc1b30f0a1ce0","observation_id":"ee9680d0-9c0b-4dfe-9d5b-192ae4190d9d","resolution":{"observed_at":"2026-08-11T21:17:13.219024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-11T21:17:13.229296Z","title":"Llama 2: Open foundation and fine-tuned chat models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.229296Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:4908cf214af527d23f9df6305c455837a930d5b3344cc6c6265b3cc20a9504b9","observation_id":"84a39f25-3c5b-4430-9629-2a4595d1e2d0","resolution":{"observed_at":"2026-08-11T21:17:13.229296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06825","last_updated":"2023-10-10T17:54:58Z","snapshot_observed_at":"2026-08-17T20:30:34.016254Z","submitted_at":"2023-10-10T17:54:58Z","title":"Mistral 7B","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06825","snapshot_observed_at":"2026-08-11T21:17:13.246285Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.246285Z"},"links":{"cited_paper":"/paper/2310.06825","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:eda7a0105c6b1e3ef1ea4ca496aa24264aa72db51d24b5687260f4218fe345d4","observation_id":"62261583-4bd8-42e1-8390-d2c9a6fd356b","resolution":{"observed_at":"2026-08-11T21:17:13.246285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:13.250510Z","title":"A diverse corpus for evaluating and developing english math word problem solvers","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.250510Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:2d8f8193107855b83b2352e3cc7c2346df89ca87f84275e9c7d263576a8a69b1","observation_id":"7a1b8704-31e0-48c1-9fdf-603319b1afc0","resolution":{"observed_at":"2026-08-11T21:17:13.250510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:17.296431Z","title":"The SMT-LIB Standard: Version 2.6","venue":null,"work_id":"601a9cec-4923-4b69-8857-564ed33298ec","year":2017},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.255217Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:24fddcb5a77c08f76fb943dca48044c57a22dfd348cdf76a51efc1724e3b51ca","observation_id":"419d693f-9eee-4cca-ba71-acf869273ad1","resolution":{"observed_at":"2026-08-11T21:17:17.308611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:17.272522Z","title":"Z3: An efficient smt solver","venue":null,"work_id":"b99b5e61-19e3-41ec-ab4d-dc251e0ee49a","year":2008},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.262568Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:b8720d73a2632dadf3425c2d5e0f44d665c9959ff90365839491bc90873bf493","observation_id":"2e68f830-4a29-4c24-8ee2-01cc1bf336d3","resolution":{"observed_at":"2026-08-11T21:17:17.280936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:13.277850Z","title":"cvc5: A versatile and industrial-strength smt solver","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.277850Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:bbf485c440f09a048bd236ca90642e12e3a58b1450d83a60df19ca58100961ec","observation_id":"fb8079e9-fbef-480f-bdfd-87d2f364b888","resolution":{"observed_at":"2026-08-11T21:17:13.277850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:17.216233Z","title":"The mathsat 4 smt solver: Tool paper","venue":null,"work_id":"2cae6a64-c672-4d16-8029-dc8ba8b0b81e","year":2008},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.290840Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:0b120e9fe8c05592acd6e9158f28e9cc7dcf5134e627fca3079c921962e82125","observation_id":"0d1071e2-af87-49ea-adf0-b5616797ef1e","resolution":{"observed_at":"2026-08-11T21:17:17.227017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:17.161555Z","title":"Sympy: symbolic computing in python","venue":null,"work_id":"719a4d7b-79e5-49a8-a8be-98b0ad954222","year":2017},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.328105Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:4a9d5d3ab7557f483eb3e377892a555a802784bb81ec8dea0fd840560389fb52","observation_id":"ccc3a315-03a6-4de1-b9b0-535f60df6f7c","resolution":{"observed_at":"2026-08-11T21:17:17.169189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:13.348581Z","title":"Scipy 1.0: fundamental algorithms for scientific computing in python","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.348581Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:67ac89bb8eed57eb166514671fcb3f93c7228cb66a3020a88b749ad87aaff79e","observation_id":"30ef39cd-b237-4be4-adf3-05380da55bd2","resolution":{"observed_at":"2026-08-11T21:17:13.348581Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:17.090279Z","title":"The strategy challenge in smt solving","venue":null,"work_id":"f6861e16-8280-4df2-97c8-ec8e11ed5347","year":2013},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.364759Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:efc8f6077b11dc8ade4eee6560a8ee93a7d0e3c73be52748f8533e01f2432f22","observation_id":"16fab5ee-7660-4ad2-992e-a9cd34a7b1ce","resolution":{"observed_at":"2026-08-11T21:17:17.101161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:17.042449Z","title":"The complexity of enumeration and reliability problems","venue":null,"work_id":"21ecdcfe-c832-45e9-9cd4-a646d95ba2da","year":1979},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.394751Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:5bad1beec3d84d7a352289676ffba7cbf023e166ee1ecf8b361428b15c11a2ed","observation_id":"fb4e2768-5ad0-4fa1-b978-876369d42a18","resolution":{"observed_at":"2026-08-11T21:17:17.068492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:17.005769Z","title":"The markov chain monte carlo method: an approach to approximate counting and integration","venue":null,"work_id":"4f80444d-9f0a-4342-8ca0-0d8d63161365","year":1996},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.434802Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:af19946fab3697dc106b466470214cd1ceecd59c81d7f76bf2d0370fa742e64a","observation_id":"5dec9c2a-058b-4cc6-adb5-42d265ce90a2","resolution":{"observed_at":"2026-08-11T21:17:17.023955Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.988672Z","title":"Uniform solution sampling using a constraint solver as an oracle","venue":null,"work_id":"ccc6e5b1-3afa-4a0d-8d25-03f441e75586","year":2012},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.494752Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:509b2e735a627fdca4fb5341091aa4f2a22abd8e293e84226f2015e829c3e0b0","observation_id":"c030ba39-cb5c-40ab-bf92-4f3bca9d859c","resolution":{"observed_at":"2026-08-11T21:17:16.993381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:13.531035Z","title":"Autoformalization with large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.531035Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:70b9aa1419877f04f7c6bf99d9b6ceadeec1d01eb019ac6273060cff50906131","observation_id":"923055ce-3701-4e71-a65d-e8b3aa6165b6","resolution":{"observed_at":"2026-08-11T21:17:13.531035Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.09583","last_updated":"2025-06-04T08:58:56Z","snapshot_observed_at":"2026-08-02T06:48:43.121988Z","submitted_at":"2023-08-18T14:23:21Z","title":"WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.09583","snapshot_observed_at":"2026-08-11T21:17:13.555980Z","title":"Wizardmath: Empowering mathematical rea- soning for large language models via reinforced evol-instruct","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.555980Z"},"links":{"cited_paper":"/paper/2308.09583","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:657087e261e1b93c98a1d1fa9127d013981475f749bcf6702182e23eafc34ccf","observation_id":"7d130101-4012-4b6a-a3b7-9a435979810e","resolution":{"observed_at":"2026-08-11T21:17:13.555980Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05506","last_updated":"2024-07-17T14:46:17Z","snapshot_observed_at":"2026-08-16T14:53:55.460770Z","submitted_at":"2023-10-09T08:18:58Z","title":"MuggleMath: Assessing the Impact of Query and Response Augmentation on Math Reasoning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.05506","snapshot_observed_at":"2026-08-11T21:17:13.576806Z","title":"Query and response augmentation cannot help out-of- domain math reasoning generalization","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.576806Z"},"links":{"cited_paper":"/paper/2310.05506","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:96aabb646d14ec811817dcfe72fd6f02aa5ae122dbcff0c4afca01cd4bb6d39b","observation_id":"8a0ea67d-8a24-4384-acaf-28410a04ab85","resolution":{"observed_at":"2026-08-11T21:17:13.576806Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05653","last_updated":"2023-10-03T02:48:42Z","snapshot_observed_at":"2026-08-07T14:43:09.380195Z","submitted_at":"2023-09-11T17:47:22Z","title":"MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.05653","snapshot_observed_at":"2026-08-11T21:17:13.627074Z","title":"Mammoth: Building math generalist models through hybrid instruction tuning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.627074Z"},"links":{"cited_paper":"/paper/2309.05653","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:0ed234b27e39762c98ac0fd5882ce8ba930b7348722aee8e091a64adfdc92b82","observation_id":"74a27026-1c1d-4fce-bb0e-f6d79b5a6530","resolution":{"observed_at":"2026-08-11T21:17:13.627074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.12284","last_updated":"2024-05-03T17:36:07Z","snapshot_observed_at":"2026-08-13T10:57:13.012119Z","submitted_at":"2023-09-21T17:45:42Z","title":"MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.12284","snapshot_observed_at":"2026-08-11T21:17:13.634393Z","title":"Metamath: Bootstrap your own mathematical questions for large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.634393Z"},"links":{"cited_paper":"/paper/2309.12284","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:0b1bc01f636ad291ce3737dced3676ebb1338165325a4eb785d6b3435b9de88d","observation_id":"9b2e21c0-25fd-47cc-b516-eab03315c8ea","resolution":{"observed_at":"2026-08-11T21:17:13.634393Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:13.664732Z","title":"Solving quantitative reasoning problems with language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.664732Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:24464c295a693b98c8c31e40b0b9a4e7e54f6d8286bfc983cce89c51ad69a02d","observation_id":"c4fcb33f-178b-43da-9b23-2462b86f1680","resolution":{"observed_at":"2026-08-11T21:17:13.664732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:13.684810Z","title":"Bleu: a method for automatic evaluation of machine translation","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.684810Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:68562a2735605ea472dfbe56e420d4f65e1243be2b1f5ffd8b81504e59234b2a","observation_id":"80987479-9e09-4406-9f8b-3e66d6b4c9ae","resolution":{"observed_at":"2026-08-11T21:17:13.684810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:13.714737Z","title":"Testing language models on a held-out high school national finals exam","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.714737Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:7e8da65a7a1305224c85c8fe998bbea5e66fbfca9454db3331b7d54dbfe5853b","observation_id":"1aea841a-4d0b-4f2b-a661-3fde24562110","resolution":{"observed_at":"2026-08-11T21:17:13.714737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.918671Z","title":"Large language models for mathematical reasoning: Progresses and challenges","venue":null,"work_id":"d0dbf7d5-4101-4a0a-a7b1-641883293719","year":2024},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.747205Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:2672110737fba75c269680a7af0dbf7b385e5c6ec5edf39152088199be995aab","observation_id":"4e989fa3-409d-462d-8d28-10b067e07e0e","resolution":{"observed_at":"2026-08-11T21:17:16.924886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.897307Z","title":"A survey of deep learning for mathematical reasoning","venue":null,"work_id":"4bff98b4-5bc5-4f3d-b553-538f570400d9","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.772818Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:0176956ebc9e840f88ef98546edae0c8e2f643b8314fcd8b42d6d8c8a7b09a01","observation_id":"45f51399-df82-4163-9a4f-c631a155ee5a","resolution":{"observed_at":"2026-08-11T21:17:16.902967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.881168Z","title":"Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts","venue":null,"work_id":"c1630f45-f447-47f3-bb1d-b14f9030c7fa","year":2024},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.787215Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:13b1aa3b188eacb44fd739d18cfce13e264be2e6b8e9b8695483aa6f18f5f50d","observation_id":"adfbb97f-3492-42f1-8a54-fd38e3d86d2e","resolution":{"observed_at":"2026-08-11T21:17:16.885894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.861277Z","title":"Large language models are zero-shot reasoners","venue":null,"work_id":"400fb522-9f45-4af3-b71a-28110bd87885","year":2022},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.803148Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:9b7f0607befc5f64e391b82f9cae948afb9a8aca57028a13f69e6ca872a4bb4d","observation_id":"6ffcc79e-104a-4e90-b406-b2539dcb416e","resolution":{"observed_at":"2026-08-11T21:17:16.868134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.842387Z","title":"Le, Ed H","venue":null,"work_id":"e6afac4c-5dde-46c7-b577-7280bf02eab3","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.811839Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:c13f132c2ed0a196ec50eb7bae1b3726ec65be39bf6030c04faeadf256d2dd96","observation_id":"e24a76b6-c439-4ee6-8014-4cfbcee8c338","resolution":{"observed_at":"2026-08-11T21:17:16.847181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.822937Z","title":"Le, and Ed H","venue":null,"work_id":"b0a54205-01bf-49fb-a58c-ec626019ee24","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.816903Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:bc4c263664fae6086cd2fb5142a0d7e33d10511bcaae7403e3a255d0496e3b50","observation_id":"c73d20fe-2dbf-4b7e-9040-5738a73c6c9e","resolution":{"observed_at":"2026-08-11T21:17:16.829072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.744820Z","title":"Decomposed prompting: A modular approach for solving complex tasks","venue":null,"work_id":"425250f2-bf3a-4d9d-bb9c-e012ec5df1de","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.826430Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:5a6c3b37d8ac3604741827bc9d872992bb89ff2f2f57f496b21a2767870d1616","observation_id":"28e535df-8bf0-4ebb-b1cb-1cbf7f10cd89","resolution":{"observed_at":"2026-08-11T21:17:16.784758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.630518Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":"199bd41e-31da-48ba-9d1e-41c70fced43a","year":2022},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.840326Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:394f73b5f939d86602c968e35b4d71dbe45b487f0136be19b326c34015d7d19e","observation_id":"2a03de57-0846-4de7-9475-91df40287514","resolution":{"observed_at":"2026-08-11T21:17:16.669194Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.527868Z","title":"Complexity-based prompting for multi-step reasoning","venue":null,"work_id":"cb1569d4-17f0-41dd-b7f4-7d0cd94f5101","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.845775Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:0a8590d17fe7e4f42870c2192d623f2e503083ff2df31fd2c618e5c20052981c","observation_id":"50f7397e-f281-4cb0-ba28-a6ed3f770169","resolution":{"observed_at":"2026-08-11T21:17:16.557002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.434860Z","title":"Automatic chain of thought prompting in large language models","venue":null,"work_id":"41992b23-1c45-4bcc-9ef8-6fe8f9f0b719","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.849785Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:4465ef1d16932b43464ce324f54997c0f78c34b44820cc8ce7b73c07e86ca748","observation_id":"89532df7-e526-4f1e-abd5-67c0f76601c5","resolution":{"observed_at":"2026-08-11T21:17:16.507462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.371597Z","title":"Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning","venue":null,"work_id":"3da81ffd-8857-4ace-b3bd-e9ad860ae134","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.867650Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:9634c6a734354aebb9e52b869f83113f46db171bfce77151e016a29c7444447c","observation_id":"561b7257-f894-41b7-9f30-9cf476c8b15f","resolution":{"observed_at":"2026-08-11T21:17:16.394140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2312.08901","last_updated":"2024-02-15T05:42:15Z","snapshot_observed_at":"2026-08-16T14:34:47.727414Z","submitted_at":"2023-12-14T13:03:13Z","title":"Fewer is More: Boosting LLM Reasoning with Reinforced Context Pruning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.08901","snapshot_observed_at":"2026-08-11T21:17:13.884758Z","title":"Boosting llm reason- ing: Push the limits of few-shot learning with reinforced in-context pruning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.884758Z"},"links":{"cited_paper":"/paper/2312.08901","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:ce3f8ee035f35c60b36bd9c13fc767c5cda73d7855b32517abb166cfe63948cf","observation_id":"da406a29-a803-4629-9e3f-fa0ae58c175e","resolution":{"observed_at":"2026-08-11T21:17:13.884758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.296290Z","title":"Teaching small language models to reason","venue":null,"work_id":"c9c6bc74-419c-4d21-9794-afbb2d04d335","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.927343Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:5f06874b941cfcada3780abfb3cd1f9b218db5368660b4c535273a01cf44e3b8","observation_id":"d03a21a3-dd4c-4018-8d5e-9deff515f01d","resolution":{"observed_at":"2026-08-11T21:17:16.308018Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:13.940437Z","title":"Scaling relationship on learning mathematical reasoning with large language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.940437Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:208083ad054118bc453ad18acba25fd757c537e28ccd0a8733589240ef537b41","observation_id":"935cb7d3-2ab8-419e-a19b-efb21ddac176","resolution":{"observed_at":"2026-08-11T21:17:13.940437Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.02144","last_updated":"2023-09-05T11:32:48Z","snapshot_observed_at":"2026-08-16T15:03:13.909629Z","submitted_at":"2023-09-05T11:32:48Z","title":"Making Large Language Models Better Reasoners with Alignment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.02144","snapshot_observed_at":"2026-08-11T21:17:13.954815Z","title":"Making large language models better reasoners with alignment","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.954815Z"},"links":{"cited_paper":"/paper/2309.02144","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:cd58a65efc2caa5271077b132d090f752fb3a53071b496fce932e58395328b3f","observation_id":"5903c622-0fc9-4bfb-8c1f-fbf9421091aa","resolution":{"observed_at":"2026-08-11T21:17:13.954815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.257600Z","title":"Large language models are better reasoners with self-verification","venue":null,"work_id":"51e12266-2edb-4636-8c7e-4207a078df12","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:13.982035Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:929526eb63819c28150df39aa34d83c033c1a678db2e8a89079754e4fc8e7665","observation_id":"d5ff79a7-cc11-4d0d-8480-b1db2c28e404","resolution":{"observed_at":"2026-08-11T21:17:16.263024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2308.07758","last_updated":"2024-06-05T03:37:35Z","snapshot_observed_at":"2026-08-16T15:08:22.772161Z","submitted_at":"2023-08-15T13:19:59Z","title":"Forward-Backward Reasoning in Large Language Models for Mathematical Verification","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.07758","snapshot_observed_at":"2026-08-11T21:17:14.002541Z","title":"Forward-backward reasoning in large language models for mathematical verification","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.002541Z"},"links":{"cited_paper":"/paper/2308.07758","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:092912b41ef56f328267b939fe1ed4881326d480bc9773fec40ca423ebc5a730","observation_id":"77cf220d-b6fe-4b19-a000-45358e91fe2b","resolution":{"observed_at":"2026-08-11T21:17:14.002541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04706","last_updated":"2024-03-07T18:00:40Z","snapshot_observed_at":"2026-08-16T14:11:52.394188Z","submitted_at":"2024-03-07T18:00:40Z","title":"Common 7B Language Models Already Possess Strong Math Capabilities","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04706","snapshot_observed_at":"2026-08-11T21:17:14.025758Z","title":"Common 7b language models already possess strong math capabilities","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.025758Z"},"links":{"cited_paper":"/paper/2403.04706","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:855b1790f3a2c513d5302168da089446ee516684f0a07c0fcf6678cf650d12d4","observation_id":"1e1ca4c4-c369-49ea-bd6e-3cf059ffb15f","resolution":{"observed_at":"2026-08-11T21:17:14.025758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.239481Z","title":null,"venue":null,"work_id":"fdf352e7-b654-455d-98e3-d32946383f86","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.034036Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:28c54972ceac49a27d63087bf463bcd5018e6c83e9bb5cbfdde2df0a4ba4d11f","observation_id":"d0a9b698-f5d0-4a8c-927c-b150c37daf3a","resolution":{"observed_at":"2026-08-11T21:17:16.247875Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.221296Z","title":"PAL: program-aided language models","venue":null,"work_id":"13239751-07b3-47ad-945f-6f2afdf8d190","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.039146Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:788bb4e291983af407e6d84bc80916931ac87244fe59150022648808c7c3d5fd","observation_id":"3cb8dd79-48da-4ccb-8dcd-fe8154c6ff7f","resolution":{"observed_at":"2026-08-11T21:17:16.229210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2310.03731","last_updated":"2023-10-05T17:52:09Z","snapshot_observed_at":"2026-08-16T14:54:42.760719Z","submitted_at":"2023-10-05T17:52:09Z","title":"MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03731","snapshot_observed_at":"2026-08-11T21:17:14.055539Z","title":"Mathcoder: Seamless code integration in llms for enhanced mathematical reasoning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.055539Z"},"links":{"cited_paper":"/paper/2310.03731","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:9a346fabe863ce81bf0d556602fdb85770c5dcfad3ba5b4711856496afce371e","observation_id":"179efcd4-c288-48e0-adf9-b6e06a64940f","resolution":{"observed_at":"2026-08-11T21:17:14.055539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.198001Z","title":"Don’t trust: Verify – grounding LLM quantitative reasoning with autoformalization","venue":null,"work_id":"3fe5aa7e-3858-4b06-94dc-07a9f4440bb6","year":2024},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.062988Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:a25a4b5d9bf784d33b0b9d250239eccbe78975f3e5fe0f62ebec96e785e9b6ae","observation_id":"1cc4bc7d-d601-44f0-85fb-0d28808253f6","resolution":{"observed_at":"2026-08-11T21:17:16.208424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2309.17452","last_updated":"2024-02-21T12:59:22Z","snapshot_observed_at":"2026-08-15T05:04:59.720326Z","submitted_at":"2023-09-29T17:59:38Z","title":"ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.17452","snapshot_observed_at":"2026-08-11T21:17:14.068963Z","title":"Tora: A tool-integrated reasoning agent for mathematical problem solving","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.068963Z"},"links":{"cited_paper":"/paper/2309.17452","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:bcf102d86a818f898d721182ac522b4487f42962e45dbc5fd1a858db0be29f97","observation_id":"af8e12f2-e23e-459a-9164-b93c22bac79d","resolution":{"observed_at":"2026-08-11T21:17:14.068963Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.152848Z","title":null,"venue":null,"work_id":"306413b0-c9af-4916-8bac-7fc927d9174e","year":2011},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.076664Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:32a2bca8c6d39eefcd20419d76afe93e161a6601a07648214a2fc511f3039af0","observation_id":"655f4159-558c-4483-9c09-ef50df050103","resolution":{"observed_at":"2026-08-11T21:17:16.185625Z","resolver_source":"raw_fallback","status":"unresolved"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.105912Z","title":"Validating smt solvers via semantic fusion","venue":null,"work_id":"6ff46d38-6b49-48b9-86ac-0554729a915b","year":2020},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.080884Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:d5edc955f7b72cb1fd7b4d5a3f50ccdc78430d8ce3004efad14f0770edd7caf6","observation_id":"d50fa9dc-0d23-4e0e-9488-d33845c02427","resolution":{"observed_at":"2026-08-11T21:17:16.124535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:14.085850Z","title":"Curriculum learning","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.085850Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:8073ea8e02dddb56c6aa8ec8e71c1165b2b46eadec58b800745e565b2bfeb07c","observation_id":"ff268928-8edb-4a3a-932a-97710af8ea2f","resolution":{"observed_at":"2026-08-11T21:17:14.085850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:14.098313Z","title":"Curriculum learning: A survey","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.098313Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:10d49c796280d593ff82c230396d6ae161c7d2774f9944c099700043e1d5d1d3","observation_id":"8db102e1-7042-4344-b84d-7d2aeacae186","resolution":{"observed_at":"2026-08-11T21:17:14.098313Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.03553","last_updated":"2024-09-27T08:16:28Z","snapshot_observed_at":"2026-08-16T13:55:00.373890Z","submitted_at":"2024-05-06T15:20:30Z","title":"AlphaMath Almost Zero: Process Supervision without Process","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.03553","snapshot_observed_at":"2026-08-11T21:17:14.104930Z","title":"Alphamath almost zero: process supervision without process","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.104930Z"},"links":{"cited_paper":"/paper/2405.03553","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:c0651d049837829340a393bfd9752ca49f3dd643fa4b525a10a9d7d3eb581c98","observation_id":"91c85a4e-e35d-4efe-a0bd-df06939c95fe","resolution":{"observed_at":"2026-08-11T21:17:14.104930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:16.042547Z","title":"Qlora: Efficient finetuning of quantized llms","venue":null,"work_id":"58898393-9db9-44ab-a764-7e11982cd344","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.110552Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:184bd4cdba5242303cc0f848cc3b30a4a9800530c91f09ffa0c0e70b590f7a9a","observation_id":"c1a7ad6c-230d-47a3-be60-48db8454fcef","resolution":{"observed_at":"2026-08-11T21:17:16.058165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.996533Z","title":"Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen","venue":null,"work_id":"59a84195-56f6-4602-9487-cecd7bdbef7c","year":2022},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.116832Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:211000e05660bd6f4743185009db7452a8d589f64c05233a9b411fed987573e7","observation_id":"d0af4971-f51e-4382-9db6-4bafc702d17f","resolution":{"observed_at":"2026-08-11T21:17:16.008447Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.931056Z","title":"Stanford alpaca: An instruction-following llama model","venue":null,"work_id":"d85f08d6-153a-44ab-ae1a-bba6681b5816","year":2023},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.123542Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:c340f2d0f01d2b16912114c83db34bc3961d77b73c5fe16342bcb66bf409f0d0","observation_id":"35713749-0cc3-404f-b52d-257ec3508b08","resolution":{"observed_at":"2026-08-11T21:17:15.969298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.820211Z","title":"Pysmt: a solver-agnostic library for fast prototyping of smt-based algorithms","venue":null,"work_id":"611e1cae-addb-44f3-9e3a-3857eb30dc38","year":2015},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.140309Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:cfb7ca538b3659a568182d7152be92dae91f9a2710d399b1026ed36dc5a4a552","observation_id":"cd1128f1-c8ee-4818-b211-303e8164ccde","resolution":{"observed_at":"2026-08-11T21:17:15.854757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:14.147370Z","title":"Array programming with numpy","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.147370Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:f6d075778c7ee258f0fcbc712ab88f612284f75c0d029f85a0f82604fe9ec44a","observation_id":"1bf880e0-eced-474a-a222-9a7d9bb4a13b","resolution":{"observed_at":"2026-08-11T21:17:14.147370Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.730333Z","title":"Xsat: a fast floating-point satisfiability solver","venue":null,"work_id":"e5dc93e0-f9c6-4d0d-bdaa-d94821b09605","year":2016},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.153641Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:088f5d13724d7a3f29683b0d071d33de2b5e5333c61c393954e7477d8040cc84","observation_id":"11955aa3-f16d-41b8-87d9-bfb404c0165b","resolution":{"observed_at":"2026-08-11T21:17:15.737452Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.704005Z","title":"Dl2: training and querying neural networks with logic","venue":null,"work_id":"87db482f-9d6c-473e-972a-42188caef8b9","year":1931},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.158658Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:8794cfcfa64b3d7b1960e0e0f669c3a102bf9681d6c15cc75c7aca1af25f68dc","observation_id":"678a2f5c-4cb9-4e8f-af9c-3feb63bb427c","resolution":{"observed_at":"2026-08-11T21:17:15.711780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.668463Z","title":"Learn- ing with logical constraints but without shortcut satisfaction","venue":null,"work_id":"e0f52cd5-5d52-47e8-abcb-02eab97df8c4","year":2022},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.162983Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:a9d047d9d18349e258cbfec9da4df59c958bf0b5d965d2034ef4cbee930c6268","observation_id":"ec0f87c4-7dc6-4e89-9d69-3a61b26adad2","resolution":{"observed_at":"2026-08-11T21:17:15.681087Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-11T21:17:14.169527Z","title":"Sara bought a pair of shoes for $50.00 and a dress for $200.00. If Rachel has twice the amount that Sara spent in total, how much is Rachel’s budget?","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.169527Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:04e3f3ae9fea79b7476aa0f4fdf32e673c7ab74d43d87f46a99d234670114efc","observation_id":"0e0cc592-d7d3-4411-81a7-b89a5070b478","resolution":{"observed_at":"2026-08-11T21:17:14.169527Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.572212Z","title":"Guidelines: • The answer NA means that the abstract and introduction do not include the claims made in the paper","venue":null,"work_id":"b8c0f1fc-415d-4f72-b51c-12c7f6ae00f1","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.176143Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:595067a8fb56cf8b1f5f65c2d946a451d2ddf90d079252b23fb4ef322de66820","observation_id":"3e5bb501-da7a-40c5-a6be-f39c5f046786","resolution":{"observed_at":"2026-08-11T21:17:15.603520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.544752Z","title":"Limitations","venue":null,"work_id":"b011627a-674f-4e0f-876b-6daa193549f6","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.188808Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:9e2ee2d16119e64383a64d2e1157fc60c08be7fb69685479a4fa262ab036052b","observation_id":"62d0b111-13b1-40dd-955f-5c509fa01f82","resolution":{"observed_at":"2026-08-11T21:17:15.561669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.472639Z","title":"Guidelines: • The answer NA means that the paper does not include theoretical results","venue":null,"work_id":"5d94bf8b-496d-448e-ad21-ba65c36181c9","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.194586Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:adc161308c3f2c12e9708521b13362b84f29afb2c4e1a8724ee42d51cdedddc4","observation_id":"71e8655d-16ae-4455-9e97-f2be193a395c","resolution":{"observed_at":"2026-08-11T21:17:15.500586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.453402Z","title":"We will public the code, as well as the fine-tuned models, for the reproducibility","venue":null,"work_id":"c21922bb-f90a-4383-9e1e-3b22e9791969","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.208068Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:e5f06997499073484242f2cfdd4d72f7cf68e69af43f6721be73dd69f59dd555","observation_id":"404ad06f-d5cf-4cf8-ad5b-383182caa2ab","resolution":{"observed_at":"2026-08-11T21:17:15.459412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.425395Z","title":"Guidelines: • The answer NA means that paper does not include experiments requiring code","venue":null,"work_id":"20874177-a76a-4ef5-904c-78ca327628eb","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.240639Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:a43b9faba582f9eb82b9fc1b4eac9ff967d1feb92b9ca1d50b06defbb5ea0479","observation_id":"be5ca655-3ce3-4318-8148-2318916673e1","resolution":{"observed_at":"2026-08-11T21:17:15.431976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.394761Z","title":"Guidelines: • The answer NA means that the paper does not include experiments","venue":null,"work_id":"a0a975d7-0af0-4368-a7dc-56860e29a953","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.250549Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:657b3535684e14dda389d8eeec8cabdee263d6ffa1cbd729ecbfa9773c0d5005","observation_id":"957e14c2-73a0-45a8-8d39-003d770fbed5","resolution":{"observed_at":"2026-08-11T21:17:15.407408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.343893Z","title":"Guidelines: • The answer NA means that the paper does not include experiments","venue":null,"work_id":"119b2314-269a-4b7e-8a48-5d06536a0bb8","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.257894Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:7dfb861ef2f551bcc16c08ec19c45d4f1d3be6d67b00e1c2b3f6d20b26184cc0","observation_id":"abb2b571-3957-4645-ad27-39063891fa1f","resolution":{"observed_at":"2026-08-11T21:17:15.355328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.282624Z","title":"Guidelines: • The answer NA means that the paper does not include experiments","venue":null,"work_id":"b59ca70b-83c9-4c48-8945-246bd1fe3cad","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.267530Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:df59c993d2e8dc34cbf5f80384ced1a89c7c7839e42709810546ce7219f10189","observation_id":"a89c9ccc-83d9-438f-a612-973480b82ba9","resolution":{"observed_at":"2026-08-11T21:17:15.314623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.256990Z","title":"Guidelines: • The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics","venue":null,"work_id":"ed5c51bc-03ae-4bd2-8853-f7ac9ea7a47b","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.277667Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:0c373640b9cbf4249489dd81b0069f8d37f1bb0b3c074e5b3c9f2b3bd8ccbb41","observation_id":"8562cca7-5aaa-4653-9821-098f9c5e00dc","resolution":{"observed_at":"2026-08-11T21:17:15.262774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.240612Z","title":"Guidelines: • The answer NA means that there is no societal impact of the work performed","venue":null,"work_id":"90f18374-3ffe-4a59-b7a0-0949771ead15","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.282149Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:461f742889ccefabdf898072e3ad6f2770d5066053d87ec3164ab68f4114d82f","observation_id":"6e4bc65b-e891-4760-9aa1-a17daef340b6","resolution":{"observed_at":"2026-08-11T21:17:15.245146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.220804Z","title":"Guidelines: • The answer NA means that the paper poses no such risks","venue":null,"work_id":"a6acce63-9e3e-4369-93de-87c2d4baad69","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.286422Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:d0d27936951fd8d59f8f513fc7d54c5a1a3aa035bb6847b0fa621e562e020693","observation_id":"9c750057-6440-4bdb-8c65-8378a0846eef","resolution":{"observed_at":"2026-08-11T21:17:15.231562Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.148615Z","title":"Guidelines: • The answer NA means that the paper does not use existing assets","venue":null,"work_id":"f6b29c35-57f3-4ac5-8dc0-0fa55686c3b3","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.291294Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:6b3e210abf076d801f045f124ce25c97893ce4002d8444e70aaccb481472986d","observation_id":"fc4fd91d-3b39-4087-800d-feff4c89eb26","resolution":{"observed_at":"2026-08-11T21:17:15.184763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.124294Z","title":"Guidelines: • The answer NA means that the paper does not release new assets","venue":null,"work_id":"9a724a17-00fc-4f05-952c-6aed4f902232","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.296731Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:d09e704ca59734bd566b02149d1d06cdb9f411db4e7ef0d489b92a16eef507d0","observation_id":"4051880e-6d1a-47a4-844e-7d89300ba7b6","resolution":{"observed_at":"2026-08-11T21:17:15.134427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:14.334754Z","title":"Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.334754Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:e0d6c473d2a4388d061d837f606f7203a0acdee859f798f8425d5ec71685a80d","observation_id":"6232d76f-ddf9-443e-b367-ca3cb8248d78","resolution":{"observed_at":"2026-08-11T21:17:14.334754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T21:17:15.082902Z","title":"Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects","venue":null,"work_id":"d4da77ef-2029-401a-aa15-9d60c5621a85","year":null},"citing_paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-11T21:17:14.346359Z"},"links":{"citing_paper":"/paper/2412.04857"},"observation_digest":"sha256:5e23469db0ca4a24cc7f7e561d83b7645876fd521ede339b38dd176c708b9cad","observation_id":"a3db7910-709a-4422-b3e8-296c4b20d55b","resolution":{"observed_at":"2026-08-11T21:17:15.089094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2412.04857","last_updated":"2024-12-06T08:49:49Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-12T04:43:14.196089Z","submitted_at":"2024-12-06T08:49:49Z","title":"Neuro-Symbolic Data Generation for Math Reasoning"},"reference_resolution":{"displayed":88,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":41,"verified_exact":1,"verified_fuzzy":45},"total_outbound_references":88},"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 88 of 88 outbound references and 3 inbound Pith citation observations for arXiv:2412.04857."}