{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TGNF4JDN7LUVDJF74G6A6TP4TB","short_pith_number":"pith:TGNF4JDN","schema_version":"1.0","canonical_sha256":"999a5e246dfae951a4bfe1bc0f4dfc984826db25a808059ab9789b0d8d096b60","source":{"kind":"arxiv","id":"2408.04472","version":2},"attestation_state":"computed","paper":{"title":"Can LLMs Beat Humans in Debating? A Dynamic Multi-agent Framework for Competitive Debate","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daling Wang, Kaisong Song, Shi Feng, Xiaocui Yang, Yifei Zhang, Yiqun Zhang","submitted_at":"2024-08-08T14:02:45Z","abstract_excerpt":"Competitive debate is a complex task of computational argumentation. Large Language Models (LLMs) suffer from hallucinations and lack competitiveness in this field. To address these challenges, we introduce Agent for Debate (Agent4Debate), a dynamic multi-agent framework based on LLMs designed to enhance their capabilities in competitive debate. Drawing inspiration from human behavior in debate preparation and execution, Agent4Debate employs a collaborative architecture where four specialized agents, involving Searcher, Analyzer, Writer, and Reviewer, dynamically interact and cooperate. These "},"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":"2408.04472","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-08T14:02:45Z","cross_cats_sorted":[],"title_canon_sha256":"6fec7836087d249d36622a0dbf7255e0080f6bc44a696e9a29b7aa1a68f04785","abstract_canon_sha256":"4f19cec887a5f40ae68c6876333ca80ad02a245de55dfad5407f91fd2b58813a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:06.474676Z","signature_b64":"7JDtzqWGTAVHLylV+QzC+IZD7qyQJ7layCBI71+bDC+Qo+c5hZEeDuHYtVId/D0Gmznd/Lge58g/5R/hYEx7DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"999a5e246dfae951a4bfe1bc0f4dfc984826db25a808059ab9789b0d8d096b60","last_reissued_at":"2026-07-05T08:57:06.474250Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:06.474250Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can LLMs Beat Humans in Debating? A Dynamic Multi-agent Framework for Competitive Debate","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daling Wang, Kaisong Song, Shi Feng, Xiaocui Yang, Yifei Zhang, Yiqun Zhang","submitted_at":"2024-08-08T14:02:45Z","abstract_excerpt":"Competitive debate is a complex task of computational argumentation. Large Language Models (LLMs) suffer from hallucinations and lack competitiveness in this field. To address these challenges, we introduce Agent for Debate (Agent4Debate), a dynamic multi-agent framework based on LLMs designed to enhance their capabilities in competitive debate. Drawing inspiration from human behavior in debate preparation and execution, Agent4Debate employs a collaborative architecture where four specialized agents, involving Searcher, Analyzer, Writer, and Reviewer, dynamically interact and cooperate. These "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.04472","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/2408.04472/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":"2408.04472","created_at":"2026-07-05T08:57:06.474307+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.04472v2","created_at":"2026-07-05T08:57:06.474307+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.04472","created_at":"2026-07-05T08:57:06.474307+00:00"},{"alias_kind":"pith_short_12","alias_value":"TGNF4JDN7LUV","created_at":"2026-07-05T08:57:06.474307+00:00"},{"alias_kind":"pith_short_16","alias_value":"TGNF4JDN7LUVDJF7","created_at":"2026-07-05T08:57:06.474307+00:00"},{"alias_kind":"pith_short_8","alias_value":"TGNF4JDN","created_at":"2026-07-05T08:57:06.474307+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.14892","citing_title":"Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems","ref_index":285,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14892","citing_title":"Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems","ref_index":284,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15607","citing_title":"Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies","ref_index":68,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TGNF4JDN7LUVDJF74G6A6TP4TB","json":"https://pith.science/pith/TGNF4JDN7LUVDJF74G6A6TP4TB.json","graph_json":"https://pith.science/api/pith-number/TGNF4JDN7LUVDJF74G6A6TP4TB/graph.json","events_json":"https://pith.science/api/pith-number/TGNF4JDN7LUVDJF74G6A6TP4TB/events.json","paper":"https://pith.science/paper/TGNF4JDN"},"agent_actions":{"view_html":"https://pith.science/pith/TGNF4JDN7LUVDJF74G6A6TP4TB","download_json":"https://pith.science/pith/TGNF4JDN7LUVDJF74G6A6TP4TB.json","view_paper":"https://pith.science/paper/TGNF4JDN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.04472&json=true","fetch_graph":"https://pith.science/api/pith-number/TGNF4JDN7LUVDJF74G6A6TP4TB/graph.json","fetch_events":"https://pith.science/api/pith-number/TGNF4JDN7LUVDJF74G6A6TP4TB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TGNF4JDN7LUVDJF74G6A6TP4TB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TGNF4JDN7LUVDJF74G6A6TP4TB/action/storage_attestation","attest_author":"https://pith.science/pith/TGNF4JDN7LUVDJF74G6A6TP4TB/action/author_attestation","sign_citation":"https://pith.science/pith/TGNF4JDN7LUVDJF74G6A6TP4TB/action/citation_signature","submit_replication":"https://pith.science/pith/TGNF4JDN7LUVDJF74G6A6TP4TB/action/replication_record"}},"created_at":"2026-07-05T08:57:06.474307+00:00","updated_at":"2026-07-05T08:57:06.474307+00:00"}