{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3DBAEGGV2XHD4Z4PDZCNTZJDM6","short_pith_number":"pith:3DBAEGGV","schema_version":"1.0","canonical_sha256":"d8c20218d5d5ce3e678f1e44d9e5236788c1ac1b2aff487d3f41a986ec5bfd4c","source":{"kind":"arxiv","id":"2502.20790","version":1},"attestation_state":"computed","paper":{"title":"Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dawei Zhu, Guangxiang Zhao, Haosheng Zou, Junfeng Ran, Lin Sun, Sujian Li, Wenhao Wu, Xiangzheng Zhang, Xiyu Wei, Xun Wang","submitted_at":"2025-02-28T07:15:12Z","abstract_excerpt":"Recent advances in Large Language Models (LLMs) have highlighted the challenge of handling long-context tasks, where models need to reason over extensive input contexts to aggregate target information. While Chain-of-Thought (CoT) prompting has shown promise for multi-step reasoning, its effectiveness for long-context scenarios remains underexplored. Through systematic investigation across diverse tasks, we demonstrate that CoT's benefits generalize across most long-context scenarios and amplify with increasing context length. Motivated by this critical observation, we propose LongRePS, a proc"},"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":"2502.20790","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-28T07:15:12Z","cross_cats_sorted":[],"title_canon_sha256":"ba1ac9cfa5924150e5ccdbc80616a8a528b9df9ea430e273bc1c7cb96a6c973d","abstract_canon_sha256":"ebdf52d4ec6d44cc19df896255b0c648f24976881d129022375d754a752b7bf4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:21:39.783542Z","signature_b64":"efWyp+6L7ZYY9y7fd3G3GG2+LZX8PXOBXuHxcOo7fvq/pCqixaVozD/HdjyRnMATW7PsJ8MtdKJXYcTEXP9VBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8c20218d5d5ce3e678f1e44d9e5236788c1ac1b2aff487d3f41a986ec5bfd4c","last_reissued_at":"2026-07-05T10:21:39.783037Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:21:39.783037Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Chain-of-Thought Matters: Improving Long-Context Language Models with Reasoning Path Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dawei Zhu, Guangxiang Zhao, Haosheng Zou, Junfeng Ran, Lin Sun, Sujian Li, Wenhao Wu, Xiangzheng Zhang, Xiyu Wei, Xun Wang","submitted_at":"2025-02-28T07:15:12Z","abstract_excerpt":"Recent advances in Large Language Models (LLMs) have highlighted the challenge of handling long-context tasks, where models need to reason over extensive input contexts to aggregate target information. While Chain-of-Thought (CoT) prompting has shown promise for multi-step reasoning, its effectiveness for long-context scenarios remains underexplored. Through systematic investigation across diverse tasks, we demonstrate that CoT's benefits generalize across most long-context scenarios and amplify with increasing context length. Motivated by this critical observation, we propose LongRePS, a proc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.20790","kind":"arxiv","version":1},"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/2502.20790/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":"2502.20790","created_at":"2026-07-05T10:21:39.783095+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.20790v1","created_at":"2026-07-05T10:21:39.783095+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.20790","created_at":"2026-07-05T10:21:39.783095+00:00"},{"alias_kind":"pith_short_12","alias_value":"3DBAEGGV2XHD","created_at":"2026-07-05T10:21:39.783095+00:00"},{"alias_kind":"pith_short_16","alias_value":"3DBAEGGV2XHD4Z4P","created_at":"2026-07-05T10:21:39.783095+00:00"},{"alias_kind":"pith_short_8","alias_value":"3DBAEGGV","created_at":"2026-07-05T10:21:39.783095+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2509.03403","citing_title":"Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training","ref_index":44,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3DBAEGGV2XHD4Z4PDZCNTZJDM6","json":"https://pith.science/pith/3DBAEGGV2XHD4Z4PDZCNTZJDM6.json","graph_json":"https://pith.science/api/pith-number/3DBAEGGV2XHD4Z4PDZCNTZJDM6/graph.json","events_json":"https://pith.science/api/pith-number/3DBAEGGV2XHD4Z4PDZCNTZJDM6/events.json","paper":"https://pith.science/paper/3DBAEGGV"},"agent_actions":{"view_html":"https://pith.science/pith/3DBAEGGV2XHD4Z4PDZCNTZJDM6","download_json":"https://pith.science/pith/3DBAEGGV2XHD4Z4PDZCNTZJDM6.json","view_paper":"https://pith.science/paper/3DBAEGGV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.20790&json=true","fetch_graph":"https://pith.science/api/pith-number/3DBAEGGV2XHD4Z4PDZCNTZJDM6/graph.json","fetch_events":"https://pith.science/api/pith-number/3DBAEGGV2XHD4Z4PDZCNTZJDM6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3DBAEGGV2XHD4Z4PDZCNTZJDM6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3DBAEGGV2XHD4Z4PDZCNTZJDM6/action/storage_attestation","attest_author":"https://pith.science/pith/3DBAEGGV2XHD4Z4PDZCNTZJDM6/action/author_attestation","sign_citation":"https://pith.science/pith/3DBAEGGV2XHD4Z4PDZCNTZJDM6/action/citation_signature","submit_replication":"https://pith.science/pith/3DBAEGGV2XHD4Z4PDZCNTZJDM6/action/replication_record"}},"created_at":"2026-07-05T10:21:39.783095+00:00","updated_at":"2026-07-05T10:21:39.783095+00:00"}