{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2CN4MVHQDDP3VTCQV5DMB5YKEH","short_pith_number":"pith:2CN4MVHQ","schema_version":"1.0","canonical_sha256":"d09bc654f018dfbacc50af46c0f70a21c226e0d56de7dc0cf57047294ac4044b","source":{"kind":"arxiv","id":"2407.14562","version":2},"attestation_state":"computed","paper":{"title":"Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Driven Prolog-based Chain-of-Thought","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"(2) Shanghai University of Engineering Science, (3) Fudan University), Chao Qu (1), Ltd., Wei Chu (1), Weidi Xu (1), Xiaoyu Tan (1), Xihe Qiu (2), Yinghui Xu (3), Yongxin Deng (2), Yuan Qi (3) ((1) INF Technology (Shanghai) Co.","submitted_at":"2024-07-18T18:52:10Z","abstract_excerpt":"Large language models (LLMs) have shown exceptional performance as general-purpose assistants, excelling across a variety of reasoning tasks. This achievement represents a significant step toward achieving artificial general intelligence (AGI). Despite these advancements, the effectiveness of LLMs often hinges on the specific prompting strategies employed, and there remains a lack of a robust framework to facilitate learning and generalization across diverse reasoning tasks. To address these challenges, we introduce a novel learning framework, THOUGHT-LIKE-PRO In this framework, we utilize imi"},"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":"2407.14562","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-18T18:52:10Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"618628a2ce5212fd0d15449bbe91a3cdf1fb9a9ed5220c29145d3c7e504dc9cd","abstract_canon_sha256":"5489c08ef3bb30293deab29080ec9d388746ae1a2cd80b1b356a9faa8645fdb8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:54:15.941871Z","signature_b64":"N9NlNguXX1CImya/oY8X9qqwq3GEw7/OZcoUnrmB404prmkS+Gtna7ztk1XIMgCPQGvQuE4hs7vlaeMWmM7qCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d09bc654f018dfbacc50af46c0f70a21c226e0d56de7dc0cf57047294ac4044b","last_reissued_at":"2026-07-05T08:54:15.941394Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:54:15.941394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Driven Prolog-based Chain-of-Thought","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"(2) Shanghai University of Engineering Science, (3) Fudan University), Chao Qu (1), Ltd., Wei Chu (1), Weidi Xu (1), Xiaoyu Tan (1), Xihe Qiu (2), Yinghui Xu (3), Yongxin Deng (2), Yuan Qi (3) ((1) INF Technology (Shanghai) Co.","submitted_at":"2024-07-18T18:52:10Z","abstract_excerpt":"Large language models (LLMs) have shown exceptional performance as general-purpose assistants, excelling across a variety of reasoning tasks. This achievement represents a significant step toward achieving artificial general intelligence (AGI). Despite these advancements, the effectiveness of LLMs often hinges on the specific prompting strategies employed, and there remains a lack of a robust framework to facilitate learning and generalization across diverse reasoning tasks. To address these challenges, we introduce a novel learning framework, THOUGHT-LIKE-PRO In this framework, we utilize imi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.14562","kind":"arxiv","version":2},"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/2407.14562/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":"2407.14562","created_at":"2026-07-05T08:54:15.941448+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.14562v2","created_at":"2026-07-05T08:54:15.941448+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.14562","created_at":"2026-07-05T08:54:15.941448+00:00"},{"alias_kind":"pith_short_12","alias_value":"2CN4MVHQDDP3","created_at":"2026-07-05T08:54:15.941448+00:00"},{"alias_kind":"pith_short_16","alias_value":"2CN4MVHQDDP3VTCQ","created_at":"2026-07-05T08:54:15.941448+00:00"},{"alias_kind":"pith_short_8","alias_value":"2CN4MVHQ","created_at":"2026-07-05T08:54:15.941448+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22485","citing_title":"VADAOrchestra: Neurosymbolic Orchestration of Adaptive Reasoning Workflows","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2606.14935","citing_title":"PrologMCP: A Standardized Prolog Tool Interface for LLM Agents","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2512.07407","citing_title":"Training Language Models to Use Prolog as a Tool","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2CN4MVHQDDP3VTCQV5DMB5YKEH","json":"https://pith.science/pith/2CN4MVHQDDP3VTCQV5DMB5YKEH.json","graph_json":"https://pith.science/api/pith-number/2CN4MVHQDDP3VTCQV5DMB5YKEH/graph.json","events_json":"https://pith.science/api/pith-number/2CN4MVHQDDP3VTCQV5DMB5YKEH/events.json","paper":"https://pith.science/paper/2CN4MVHQ"},"agent_actions":{"view_html":"https://pith.science/pith/2CN4MVHQDDP3VTCQV5DMB5YKEH","download_json":"https://pith.science/pith/2CN4MVHQDDP3VTCQV5DMB5YKEH.json","view_paper":"https://pith.science/paper/2CN4MVHQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.14562&json=true","fetch_graph":"https://pith.science/api/pith-number/2CN4MVHQDDP3VTCQV5DMB5YKEH/graph.json","fetch_events":"https://pith.science/api/pith-number/2CN4MVHQDDP3VTCQV5DMB5YKEH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2CN4MVHQDDP3VTCQV5DMB5YKEH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2CN4MVHQDDP3VTCQV5DMB5YKEH/action/storage_attestation","attest_author":"https://pith.science/pith/2CN4MVHQDDP3VTCQV5DMB5YKEH/action/author_attestation","sign_citation":"https://pith.science/pith/2CN4MVHQDDP3VTCQV5DMB5YKEH/action/citation_signature","submit_replication":"https://pith.science/pith/2CN4MVHQDDP3VTCQV5DMB5YKEH/action/replication_record"}},"created_at":"2026-07-05T08:54:15.941448+00:00","updated_at":"2026-07-05T08:54:15.941448+00:00"}