{"paper":{"title":"LLM Multi-Agent Systems: Challenges and Open Problems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Multi-agent LLM systems can solve complex tasks through agent collaboration but leave several challenges inadequately addressed.","cross_cats":["cs.AI"],"primary_cat":"cs.MA","authors_text":"Qifan Zhang, Shanshan Han, Weizhao Jin, Zhaozhuo Xu","submitted_at":"2024-02-05T23:06:42Z","abstract_excerpt":"This paper explores multi-agent systems and identify challenges that remain inadequately addressed. By leveraging the diverse capabilities and roles of individual agents, multi-agent systems can tackle complex tasks through agent collaboration. We discuss optimizing task allocation, fostering robust reasoning through iterative debates, managing complex and layered context information, and enhancing memory management to support the intricate interactions within multi-agent systems. We also explore potential applications of multi-agent systems in blockchain systems to shed light on their future "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"By leveraging the diverse capabilities and roles of individual agents, multi-agent systems can tackle complex tasks through agent collaboration, yet several challenges remain inadequately addressed including task allocation, robust reasoning through iterative debates, complex context management, memory management, and blockchain applications.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that the listed challenges (task allocation, iterative debates, context and memory management, blockchain uses) are currently inadequately addressed and that discussing them will meaningfully guide future development, without providing systematic evidence or citations quantifying the inadequacy.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"The paper identifies inadequately addressed challenges in optimizing task allocation, fostering robust reasoning through debates, managing layered context, enhancing memory, and applying multi-agent systems to blockchain.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Multi-agent LLM systems can solve complex tasks through agent collaboration but leave several challenges inadequately addressed.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3d7fadc1b23a45dd233b48d55960356616c264484257af296770cba0e010f555"},"source":{"id":"2402.03578","kind":"arxiv","version":3},"verdict":{"id":"0179cda0-9363-4ad0-9ed2-4f813d00dff2","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T10:18:34.277186Z","strongest_claim":"By leveraging the diverse capabilities and roles of individual agents, multi-agent systems can tackle complex tasks through agent collaboration, yet several challenges remain inadequately addressed including task allocation, robust reasoning through iterative debates, complex context management, memory management, and blockchain applications.","one_line_summary":"The paper identifies inadequately addressed challenges in optimizing task allocation, fostering robust reasoning through debates, managing layered context, enhancing memory, and applying multi-agent systems to blockchain.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that the listed challenges (task allocation, iterative debates, context and memory management, blockchain uses) are currently inadequately addressed and that discussing them will meaningfully guide future development, without providing systematic evidence or citations quantifying the inadequacy.","pith_extraction_headline":"Multi-agent LLM systems can solve complex tasks through agent collaboration but leave several challenges inadequately addressed."},"references":{"count":50,"sample":[{"doi":"","year":null,"title":"Evil geniuses: Delving into the safety of llm-based agents","work_id":"e13aaabf-88ce-407e-8517-ac57b98525d5","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Identifying the Risks of LM Agents with an LM-Emulated Sandbox","work_id":"3d4c3b66-d749-4939-b1bc-62b10b2ebbb6","ref_index":2,"cited_arxiv_id":"2309.15817","is_internal_anchor":true},{"doi":"","year":null,"title":"Igniting language intelligence: The hitchhiker’s guide from chain-of-thought reasoning to language agents","work_id":"0ca205d0-2b2a-4432-aab2-3f1919273429","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"R-judge: Benchmarking safety risk awareness for llm agents","work_id":"80797370-dc29-417d-83c5-ec1313a64166","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Multi-Agent Security Workshop@ NeurIPS'23 , year=","work_id":"920fa8f5-fd08-443b-a487-9140bc0c7824","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":50,"snapshot_sha256":"9cac8eeb94dda39bd0ef469e4c12a8428c9464d97c9653660bc14cf6f7ac99f6","internal_anchors":15},"formal_canon":{"evidence_count":2,"snapshot_sha256":"b26bba33b0c7518cfd369e4f2133fba5b2c12646f30de367a2116a4932b26709"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}