{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FTPUOHKYCVKCYILFW7RG3MBUXE","short_pith_number":"pith:FTPUOHKY","schema_version":"1.0","canonical_sha256":"2cdf471d5815542c2165b7e26db034b93ccf467f2c5232758035d13eb79d9659","source":{"kind":"arxiv","id":"2409.11527","version":2},"attestation_state":"computed","paper":{"title":"Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Anthony Rios, Fatemeh Haji, Jason Chiang, Maryam Tabar, Mazal Bethany, Peyman Najafirad","submitted_at":"2024-09-17T19:54:37Z","abstract_excerpt":"Multi-agent strategies have emerged as a promising approach to enhance the reasoning abilities of Large Language Models (LLMs) by assigning specialized roles in the problem-solving process. Concurrently, Tree of Thoughts (ToT) methods have shown potential in improving reasoning for complex question-answering tasks by exploring diverse reasoning paths. A critical limitation in multi-agent reasoning is the 'Reasoner' agent's shallow exploration of reasoning paths. While ToT strategies could help mitigate this problem, they may generate flawed reasoning branches, which could harm the trustworthin"},"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":"2409.11527","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-09-17T19:54:37Z","cross_cats_sorted":[],"title_canon_sha256":"ab7a6d7f171d1fd9df444b1bacbcc8b461580ddc534ea49084c2729fd7d88ac9","abstract_canon_sha256":"d70fa35b5e1930db59a87df28171ecf945f76cee65c3aad30dc7cee5e0c18ce0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:31:08.619097Z","signature_b64":"wjJk7nAo/hF/T9BqvkojKvq8c3cFVSm9hqbk2ca63bb8hkOEq0YgzgtBTZaQ2PeF0m5oio2wPSoU+gADbAOWDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cdf471d5815542c2165b7e26db034b93ccf467f2c5232758035d13eb79d9659","last_reissued_at":"2026-07-05T09:31:08.618588Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:31:08.618588Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Anthony Rios, Fatemeh Haji, Jason Chiang, Maryam Tabar, Mazal Bethany, Peyman Najafirad","submitted_at":"2024-09-17T19:54:37Z","abstract_excerpt":"Multi-agent strategies have emerged as a promising approach to enhance the reasoning abilities of Large Language Models (LLMs) by assigning specialized roles in the problem-solving process. Concurrently, Tree of Thoughts (ToT) methods have shown potential in improving reasoning for complex question-answering tasks by exploring diverse reasoning paths. A critical limitation in multi-agent reasoning is the 'Reasoner' agent's shallow exploration of reasoning paths. While ToT strategies could help mitigate this problem, they may generate flawed reasoning branches, which could harm the trustworthin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.11527","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/2409.11527/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":"2409.11527","created_at":"2026-07-05T09:31:08.618655+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.11527v2","created_at":"2026-07-05T09:31:08.618655+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.11527","created_at":"2026-07-05T09:31:08.618655+00:00"},{"alias_kind":"pith_short_12","alias_value":"FTPUOHKYCVKC","created_at":"2026-07-05T09:31:08.618655+00:00"},{"alias_kind":"pith_short_16","alias_value":"FTPUOHKYCVKCYILF","created_at":"2026-07-05T09:31:08.618655+00:00"},{"alias_kind":"pith_short_8","alias_value":"FTPUOHKY","created_at":"2026-07-05T09:31:08.618655+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2601.20352","citing_title":"AMA: Adaptive Memory via Multi-Agent Collaboration","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2503.13657","citing_title":"Why Do Multi-Agent LLM Systems Fail?","ref_index":67,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FTPUOHKYCVKCYILFW7RG3MBUXE","json":"https://pith.science/pith/FTPUOHKYCVKCYILFW7RG3MBUXE.json","graph_json":"https://pith.science/api/pith-number/FTPUOHKYCVKCYILFW7RG3MBUXE/graph.json","events_json":"https://pith.science/api/pith-number/FTPUOHKYCVKCYILFW7RG3MBUXE/events.json","paper":"https://pith.science/paper/FTPUOHKY"},"agent_actions":{"view_html":"https://pith.science/pith/FTPUOHKYCVKCYILFW7RG3MBUXE","download_json":"https://pith.science/pith/FTPUOHKYCVKCYILFW7RG3MBUXE.json","view_paper":"https://pith.science/paper/FTPUOHKY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.11527&json=true","fetch_graph":"https://pith.science/api/pith-number/FTPUOHKYCVKCYILFW7RG3MBUXE/graph.json","fetch_events":"https://pith.science/api/pith-number/FTPUOHKYCVKCYILFW7RG3MBUXE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FTPUOHKYCVKCYILFW7RG3MBUXE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FTPUOHKYCVKCYILFW7RG3MBUXE/action/storage_attestation","attest_author":"https://pith.science/pith/FTPUOHKYCVKCYILFW7RG3MBUXE/action/author_attestation","sign_citation":"https://pith.science/pith/FTPUOHKYCVKCYILFW7RG3MBUXE/action/citation_signature","submit_replication":"https://pith.science/pith/FTPUOHKYCVKCYILFW7RG3MBUXE/action/replication_record"}},"created_at":"2026-07-05T09:31:08.618655+00:00","updated_at":"2026-07-05T09:31:08.618655+00:00"}