{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SZVOYTKKL5JCAAMBWJZFDBWADY","short_pith_number":"pith:SZVOYTKK","schema_version":"1.0","canonical_sha256":"966aec4d4a5f52200181b2725186c01e1b473f2ddcd4c1f5882f6450734862c9","source":{"kind":"arxiv","id":"2509.06284","version":1},"attestation_state":"computed","paper":{"title":"From Implicit Exploration to Structured Reasoning: Leveraging Guideline and Refinement for LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Jiaxiang Chen, Mingxi Zou, Song Wang, Zenglin Xu, Zhijian Zhou, Zhucong Li, Zhuo Wang","submitted_at":"2025-09-08T02:11:49Z","abstract_excerpt":"Large language models (LLMs) have advanced general-purpose reasoning, showing strong performance across diverse tasks. However, existing methods often rely on implicit exploration, where the model follows stochastic and unguided reasoning paths-like walking without a map. This leads to unstable reasoning paths, lack of error correction, and limited learning from past experience. To address these issues, we propose a framework that shifts from implicit exploration to structured reasoning through guideline and refinement. First, we extract structured reasoning patterns from successful trajectori"},"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":"2509.06284","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-09-08T02:11:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1919815edd7a28c5f1e338e33b90a074501e2b2909e69a9de7ea51bc214bd29a","abstract_canon_sha256":"df2589a308bf7627766f5960cd9a3c2f8149684209aa7794014b3ca79f240000"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:33.027678Z","signature_b64":"8SRXO5zmh1byPk/Eg8sR+s0efHoLClPREPL/6FBi2N6nHgRvm7hrAFs/z3A7BVdq1TtS4VHATpVQNroCwENiBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"966aec4d4a5f52200181b2725186c01e1b473f2ddcd4c1f5882f6450734862c9","last_reissued_at":"2026-07-05T12:06:33.027142Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:33.027142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Implicit Exploration to Structured Reasoning: Leveraging Guideline and Refinement for LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Jiaxiang Chen, Mingxi Zou, Song Wang, Zenglin Xu, Zhijian Zhou, Zhucong Li, Zhuo Wang","submitted_at":"2025-09-08T02:11:49Z","abstract_excerpt":"Large language models (LLMs) have advanced general-purpose reasoning, showing strong performance across diverse tasks. However, existing methods often rely on implicit exploration, where the model follows stochastic and unguided reasoning paths-like walking without a map. This leads to unstable reasoning paths, lack of error correction, and limited learning from past experience. To address these issues, we propose a framework that shifts from implicit exploration to structured reasoning through guideline and refinement. First, we extract structured reasoning patterns from successful trajectori"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.06284","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/2509.06284/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":"2509.06284","created_at":"2026-07-05T12:06:33.027207+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.06284v1","created_at":"2026-07-05T12:06:33.027207+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.06284","created_at":"2026-07-05T12:06:33.027207+00:00"},{"alias_kind":"pith_short_12","alias_value":"SZVOYTKKL5JC","created_at":"2026-07-05T12:06:33.027207+00:00"},{"alias_kind":"pith_short_16","alias_value":"SZVOYTKKL5JCAAMB","created_at":"2026-07-05T12:06:33.027207+00:00"},{"alias_kind":"pith_short_8","alias_value":"SZVOYTKK","created_at":"2026-07-05T12:06:33.027207+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SZVOYTKKL5JCAAMBWJZFDBWADY","json":"https://pith.science/pith/SZVOYTKKL5JCAAMBWJZFDBWADY.json","graph_json":"https://pith.science/api/pith-number/SZVOYTKKL5JCAAMBWJZFDBWADY/graph.json","events_json":"https://pith.science/api/pith-number/SZVOYTKKL5JCAAMBWJZFDBWADY/events.json","paper":"https://pith.science/paper/SZVOYTKK"},"agent_actions":{"view_html":"https://pith.science/pith/SZVOYTKKL5JCAAMBWJZFDBWADY","download_json":"https://pith.science/pith/SZVOYTKKL5JCAAMBWJZFDBWADY.json","view_paper":"https://pith.science/paper/SZVOYTKK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.06284&json=true","fetch_graph":"https://pith.science/api/pith-number/SZVOYTKKL5JCAAMBWJZFDBWADY/graph.json","fetch_events":"https://pith.science/api/pith-number/SZVOYTKKL5JCAAMBWJZFDBWADY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SZVOYTKKL5JCAAMBWJZFDBWADY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SZVOYTKKL5JCAAMBWJZFDBWADY/action/storage_attestation","attest_author":"https://pith.science/pith/SZVOYTKKL5JCAAMBWJZFDBWADY/action/author_attestation","sign_citation":"https://pith.science/pith/SZVOYTKKL5JCAAMBWJZFDBWADY/action/citation_signature","submit_replication":"https://pith.science/pith/SZVOYTKKL5JCAAMBWJZFDBWADY/action/replication_record"}},"created_at":"2026-07-05T12:06:33.027207+00:00","updated_at":"2026-07-05T12:06:33.027207+00:00"}