{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PC4DACBTRCWQQAOX3HAXNIQMTI","short_pith_number":"pith:PC4DACBT","schema_version":"1.0","canonical_sha256":"78b830083388ad0801d7d9c176a20c9a15ad56f2d4976ef2632a118194599362","source":{"kind":"arxiv","id":"2412.17481","version":2},"attestation_state":"computed","paper":{"title":"A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.CL","authors_text":"Shuaihang Chen, Ting Liu, Wei Han, Weinan Zhang, Yuanxing Liu","submitted_at":"2024-12-23T11:11:51Z","abstract_excerpt":"LLM-based Multi-Agent Systems ( LLM-MAS ) have become a research hotspot since the rise of large language models (LLMs). However, with the continuous influx of new related works, the existing reviews struggle to capture them comprehensively. This paper presents a comprehensive survey of these studies. We first discuss the definition of LLM-MAS, a framework encompassing much of previous work. We provide an overview of the various applications of LLM-MAS in (i) solving complex tasks, (ii) simulating specific scenarios, and (iii) evaluating generative agents. Building on previous studies, we also"},"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":"2412.17481","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-23T11:11:51Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"99504ff1133aae7230a169957139ac503addc248dc121e2363edc02b595601f5","abstract_canon_sha256":"b2c8e34e3897c44a5e2556e7de4fcb06d1770da4447df99582b9e69c791455da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:57:49.606471Z","signature_b64":"GtUg0uD+15bYmEF1Gc401rqXu1Zvh8Yubsfkr/V1yN4Vd9p6Qg3810EKR6EqEHaxYDks8sxp29XLT/2OREQ/AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78b830083388ad0801d7d9c176a20c9a15ad56f2d4976ef2632a118194599362","last_reissued_at":"2026-07-05T09:57:49.605872Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:57:49.605872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.CL","authors_text":"Shuaihang Chen, Ting Liu, Wei Han, Weinan Zhang, Yuanxing Liu","submitted_at":"2024-12-23T11:11:51Z","abstract_excerpt":"LLM-based Multi-Agent Systems ( LLM-MAS ) have become a research hotspot since the rise of large language models (LLMs). However, with the continuous influx of new related works, the existing reviews struggle to capture them comprehensively. This paper presents a comprehensive survey of these studies. We first discuss the definition of LLM-MAS, a framework encompassing much of previous work. We provide an overview of the various applications of LLM-MAS in (i) solving complex tasks, (ii) simulating specific scenarios, and (iii) evaluating generative agents. Building on previous studies, we also"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.17481","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/2412.17481/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":"2412.17481","created_at":"2026-07-05T09:57:49.605955+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.17481v2","created_at":"2026-07-05T09:57:49.605955+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.17481","created_at":"2026-07-05T09:57:49.605955+00:00"},{"alias_kind":"pith_short_12","alias_value":"PC4DACBTRCWQ","created_at":"2026-07-05T09:57:49.605955+00:00"},{"alias_kind":"pith_short_16","alias_value":"PC4DACBTRCWQQAOX","created_at":"2026-07-05T09:57:49.605955+00:00"},{"alias_kind":"pith_short_8","alias_value":"PC4DACBT","created_at":"2026-07-05T09:57:49.605955+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":10,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23664","citing_title":"MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?","ref_index":42,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08274","citing_title":"Toward Human-Centered Multi-Agent Systems: Integrating Cognition, Culture, Values, and Cooperation in AI Agents","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2606.02359","citing_title":"MOC: Multi-Order Communication in LLM-based Multi-Agent Systems","ref_index":62,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24823","citing_title":"Agent Manufacturing: Foundation-Model Agents as First-Class Industrial Entities","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30246","citing_title":"Clarus: Coordinating Autonomous Research Agents toward Web-Scale Scientific Collaboration","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25929","citing_title":"Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2511.15408","citing_title":"Chinese Short-Form Creative Content Generation via Explanation-Oriented Multi-Objective Optimization","ref_index":75,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15028","citing_title":"Multi-Agentic Approach for History Matching of Oil Reservoirs","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13725","citing_title":"ScioMind: Cognitively Grounded Multi-Agent Social Simulation with Anchoring-Based Belief Dynamics and Dynamic Profiles","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02801","citing_title":"Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PC4DACBTRCWQQAOX3HAXNIQMTI","json":"https://pith.science/pith/PC4DACBTRCWQQAOX3HAXNIQMTI.json","graph_json":"https://pith.science/api/pith-number/PC4DACBTRCWQQAOX3HAXNIQMTI/graph.json","events_json":"https://pith.science/api/pith-number/PC4DACBTRCWQQAOX3HAXNIQMTI/events.json","paper":"https://pith.science/paper/PC4DACBT"},"agent_actions":{"view_html":"https://pith.science/pith/PC4DACBTRCWQQAOX3HAXNIQMTI","download_json":"https://pith.science/pith/PC4DACBTRCWQQAOX3HAXNIQMTI.json","view_paper":"https://pith.science/paper/PC4DACBT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.17481&json=true","fetch_graph":"https://pith.science/api/pith-number/PC4DACBTRCWQQAOX3HAXNIQMTI/graph.json","fetch_events":"https://pith.science/api/pith-number/PC4DACBTRCWQQAOX3HAXNIQMTI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PC4DACBTRCWQQAOX3HAXNIQMTI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PC4DACBTRCWQQAOX3HAXNIQMTI/action/storage_attestation","attest_author":"https://pith.science/pith/PC4DACBTRCWQQAOX3HAXNIQMTI/action/author_attestation","sign_citation":"https://pith.science/pith/PC4DACBTRCWQQAOX3HAXNIQMTI/action/citation_signature","submit_replication":"https://pith.science/pith/PC4DACBTRCWQQAOX3HAXNIQMTI/action/replication_record"}},"created_at":"2026-07-05T09:57:49.605955+00:00","updated_at":"2026-07-05T09:57:49.605955+00:00"}