{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:MVWG4TL7UMYSOYCSH2GYYWIDJZ","short_pith_number":"pith:MVWG4TL7","schema_version":"1.0","canonical_sha256":"656c6e4d7fa3312760523e8d8c59034e4123f4a2a8c7d04b213cca76c168703a","source":{"kind":"arxiv","id":"2305.16582","version":2},"attestation_state":"computed","paper":{"title":"Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hai Zhao, Yao Yao, Zuchao Li","submitted_at":"2023-05-26T02:15:09Z","abstract_excerpt":"With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinki"},"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":"2305.16582","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-26T02:15:09Z","cross_cats_sorted":[],"title_canon_sha256":"a1151c8298b561534f24e2c6eeaaf262e216bff80a58650e019d7a9ca60f2ea8","abstract_canon_sha256":"d7eaed72bf46e7be4360dd94d48fd02bfec5ac68e562fe78ecf20740b2ea95a2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:59:50.797241Z","signature_b64":"utkBOjr8P225I6uD2nYOUEGyAgq65YGX/SvoVubT8AE56NEle+l0FHusNZlHAzWdxw0aJFpEbQH7+3QPQNPmAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"656c6e4d7fa3312760523e8d8c59034e4123f4a2a8c7d04b213cca76c168703a","last_reissued_at":"2026-07-05T07:59:50.796841Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:59:50.796841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hai Zhao, Yao Yao, Zuchao Li","submitted_at":"2023-05-26T02:15:09Z","abstract_excerpt":"With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinki"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.16582","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/2305.16582/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":"2305.16582","created_at":"2026-07-05T07:59:50.796899+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.16582v2","created_at":"2026-07-05T07:59:50.796899+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.16582","created_at":"2026-07-05T07:59:50.796899+00:00"},{"alias_kind":"pith_short_12","alias_value":"MVWG4TL7UMYS","created_at":"2026-07-05T07:59:50.796899+00:00"},{"alias_kind":"pith_short_16","alias_value":"MVWG4TL7UMYSOYCS","created_at":"2026-07-05T07:59:50.796899+00:00"},{"alias_kind":"pith_short_8","alias_value":"MVWG4TL7","created_at":"2026-07-05T07:59:50.796899+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.08009","citing_title":"From Execution to Education: A Bloom-Aligned Framework for Measuring Educational Control in LLMs","ref_index":195,"is_internal_anchor":true},{"citing_arxiv_id":"2410.04047","citing_title":"TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2509.03540","citing_title":"Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2503.12605","citing_title":"Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey","ref_index":134,"is_internal_anchor":false},{"citing_arxiv_id":"2502.17419","citing_title":"From System 1 to System 2: A Survey of Reasoning Large Language Models","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2402.07927","citing_title":"A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07321","citing_title":"Syntax Is Easy, Semantics Is Hard: Evaluating LLMs for LTL Translation","ref_index":67,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17800","citing_title":"ReFineVLA: Multimodal Reasoning-Aware Generalist Robotic Policies via Teacher-Guided Fine-Tuning","ref_index":43,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02035","citing_title":"VIDA: A dataset for Visually Dependent Ambiguity in Multimodal Machine Translation","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MVWG4TL7UMYSOYCSH2GYYWIDJZ","json":"https://pith.science/pith/MVWG4TL7UMYSOYCSH2GYYWIDJZ.json","graph_json":"https://pith.science/api/pith-number/MVWG4TL7UMYSOYCSH2GYYWIDJZ/graph.json","events_json":"https://pith.science/api/pith-number/MVWG4TL7UMYSOYCSH2GYYWIDJZ/events.json","paper":"https://pith.science/paper/MVWG4TL7"},"agent_actions":{"view_html":"https://pith.science/pith/MVWG4TL7UMYSOYCSH2GYYWIDJZ","download_json":"https://pith.science/pith/MVWG4TL7UMYSOYCSH2GYYWIDJZ.json","view_paper":"https://pith.science/paper/MVWG4TL7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.16582&json=true","fetch_graph":"https://pith.science/api/pith-number/MVWG4TL7UMYSOYCSH2GYYWIDJZ/graph.json","fetch_events":"https://pith.science/api/pith-number/MVWG4TL7UMYSOYCSH2GYYWIDJZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MVWG4TL7UMYSOYCSH2GYYWIDJZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MVWG4TL7UMYSOYCSH2GYYWIDJZ/action/storage_attestation","attest_author":"https://pith.science/pith/MVWG4TL7UMYSOYCSH2GYYWIDJZ/action/author_attestation","sign_citation":"https://pith.science/pith/MVWG4TL7UMYSOYCSH2GYYWIDJZ/action/citation_signature","submit_replication":"https://pith.science/pith/MVWG4TL7UMYSOYCSH2GYYWIDJZ/action/replication_record"}},"created_at":"2026-07-05T07:59:50.796899+00:00","updated_at":"2026-07-05T07:59:50.796899+00:00"}