{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RXGFDHZJO4AYTFY7FKBJBR2FGC","short_pith_number":"pith:RXGFDHZJ","schema_version":"1.0","canonical_sha256":"8dcc519f29770189971f2a8290c74530926280f41ff1c42a100e4ddb199650ce","source":{"kind":"arxiv","id":"2502.08127","version":3},"attestation_state":"computed","paper":{"title":"Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Han Yi, Jian-Yun Nie, Jimin Huang, Lingfei Qian, Qianqian Xie, Weipeng Zhou, Xueqing Peng, Yan Wang, Yilun Zhao","submitted_at":"2025-02-12T05:13:04Z","abstract_excerpt":"As the fundamental capability behind decision-making in finance, financial reasoning poses distinct challenges for LLMs. Although reinforcement learning (RL) have boosted generic reasoning, the progress in finance is hindered by the absence of empirical study of building effective financial chain-of-thought (CoT) corpus, a systematic comparison of different RL methods, and comprehensive benchmarks. To address these gaps, we introduce FinCoT, the first open high-fidelity CoT corpus for finance, distilled from seven QA datasets by a novel three-stage pipeline that incorporates domain supervision"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2502.08127","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-02-12T05:13:04Z","cross_cats_sorted":[],"title_canon_sha256":"4f06bb0886af7f4f74f1447e8b9194a7a18f45877d744a0f1a6b96f8ba58c0af","abstract_canon_sha256":"f0ff0c3183893f78cf557da1d3597a0d0845aa38f82fee98c1271bf76c9992dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:42.148058Z","signature_b64":"MnS5axPAcXgY6dRLmy5WcNlKPAEP8Yic4usgKIrC1DDd9FCpq1+NNFN1/WMSxyYqlFBvBvkFKWvOI33E31+tBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8dcc519f29770189971f2a8290c74530926280f41ff1c42a100e4ddb199650ce","last_reissued_at":"2026-07-05T11:21:42.147508Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:42.147508Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fino1: On the Transferability of Reasoning-Enhanced LLMs and Reinforcement Learning to Finance","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Han Yi, Jian-Yun Nie, Jimin Huang, Lingfei Qian, Qianqian Xie, Weipeng Zhou, Xueqing Peng, Yan Wang, Yilun Zhao","submitted_at":"2025-02-12T05:13:04Z","abstract_excerpt":"As the fundamental capability behind decision-making in finance, financial reasoning poses distinct challenges for LLMs. Although reinforcement learning (RL) have boosted generic reasoning, the progress in finance is hindered by the absence of empirical study of building effective financial chain-of-thought (CoT) corpus, a systematic comparison of different RL methods, and comprehensive benchmarks. To address these gaps, we introduce FinCoT, the first open high-fidelity CoT corpus for finance, distilled from seven QA datasets by a novel three-stage pipeline that incorporates domain supervision"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.08127","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.08127/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.08127","created_at":"2026-07-05T11:21:42.147573+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.08127v3","created_at":"2026-07-05T11:21:42.147573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.08127","created_at":"2026-07-05T11:21:42.147573+00:00"},{"alias_kind":"pith_short_12","alias_value":"RXGFDHZJO4AY","created_at":"2026-07-05T11:21:42.147573+00:00"},{"alias_kind":"pith_short_16","alias_value":"RXGFDHZJO4AYTFY7","created_at":"2026-07-05T11:21:42.147573+00:00"},{"alias_kind":"pith_short_8","alias_value":"RXGFDHZJ","created_at":"2026-07-05T11:21:42.147573+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11537","citing_title":"MoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03031","citing_title":"AUDITFLOW: Executable Symbolic Environments for Structured Financial Reporting Verification","ref_index":75,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31608","citing_title":"CLExEval: A Human-in-the-Loop Framework for Qualitative Evaluation of LLM Clinical Reasoning","ref_index":102,"is_internal_anchor":false},{"citing_arxiv_id":"2605.26074","citing_title":"StakeBench: Evaluating Language Understanding Grounded in Market Commitment","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2504.02181","citing_title":"A Survey of Scaling in Large Language Model Reasoning","ref_index":158,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21975","citing_title":"Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2505.20650","citing_title":"FinTagging: Benchmarking LLMs for Extracting and Structuring Financial Information","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2510.08886","citing_title":"FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2602.16990","citing_title":"Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20284","citing_title":"JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18272","citing_title":"MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model","ref_index":42,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RXGFDHZJO4AYTFY7FKBJBR2FGC","json":"https://pith.science/pith/RXGFDHZJO4AYTFY7FKBJBR2FGC.json","graph_json":"https://pith.science/api/pith-number/RXGFDHZJO4AYTFY7FKBJBR2FGC/graph.json","events_json":"https://pith.science/api/pith-number/RXGFDHZJO4AYTFY7FKBJBR2FGC/events.json","paper":"https://pith.science/paper/RXGFDHZJ"},"agent_actions":{"view_html":"https://pith.science/pith/RXGFDHZJO4AYTFY7FKBJBR2FGC","download_json":"https://pith.science/pith/RXGFDHZJO4AYTFY7FKBJBR2FGC.json","view_paper":"https://pith.science/paper/RXGFDHZJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.08127&json=true","fetch_graph":"https://pith.science/api/pith-number/RXGFDHZJO4AYTFY7FKBJBR2FGC/graph.json","fetch_events":"https://pith.science/api/pith-number/RXGFDHZJO4AYTFY7FKBJBR2FGC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RXGFDHZJO4AYTFY7FKBJBR2FGC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RXGFDHZJO4AYTFY7FKBJBR2FGC/action/storage_attestation","attest_author":"https://pith.science/pith/RXGFDHZJO4AYTFY7FKBJBR2FGC/action/author_attestation","sign_citation":"https://pith.science/pith/RXGFDHZJO4AYTFY7FKBJBR2FGC/action/citation_signature","submit_replication":"https://pith.science/pith/RXGFDHZJO4AYTFY7FKBJBR2FGC/action/replication_record"}},"created_at":"2026-07-05T11:21:42.147573+00:00","updated_at":"2026-07-05T11:21:42.147573+00:00"}