{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/2408-00989","title":"On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents","arxiv_id":"2408.00989","date":"2024-08-02","proceeding":null,"authors":["Jen-tse Huang","Jiaxu Zhou","Tailin Jin","Xuhui Zhou","Zixi Chen","Wenxuan Wang","Youliang Yuan","Michael R. Lyu","Maarten Sap"],"abstract":"Large language model-based multi-agent systems have shown great abilities across various tasks due to the collaboration of expert agents, each focusing on a specific domain. However, the impact of clumsy or even malicious agents, i.e., those who frequently make errors in their tasks, on the overall performance of the system remains underexplored. This paper investigates: (1) What is the resilience of various system structures (e.g., A$\\rightarrow$B$\\rightarrow$C, A$\\leftrightarrow$B$\\leftrightarrow$C) under faulty agents, on different downstream tasks? (2) How can we increase system resilience to defend against these agents? To simulate faulty agents, we propose two approaches, AutoTransform and AutoInject, which introduce mistakes into the agents' responses. We select four downstream tasks, including code generation, math problems, translation, and text evaluation. Results suggest that the hierarchical structure, i.e., A$\\rightarrow$(B$\\leftrightarrow$C), exhibits superior resilience with the lowest performance drop of $9.2\\%$, compared to $26.0\\%$ and $31.2\\%$ of other two structures. Additionally, we improve the system resilience with two methods, introducing a mechanism for each agent to challenge others' outputs, and an additional agent to review and correct messages. Our code and data are available at https://github.com/CUHK-ARISE/MAS-Resilience.","url_abs":"https://arxiv.org/abs/2408.00989v3","url_pdf":"https://arxiv.org/pdf/2408.00989v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"2408-00989","repo_url":"https://github.com/cuhk-arise/mas-resilience","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"code-generation","task_name":"Code Generation"},{"task_slug":"large-language-model","task_name":"Large Language Model"},{"task_slug":"math","task_name":"Math"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2408.00989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.00989"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/cuhk-arise/mas-resilience","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"4f8cdccde1efa523","entry":"FormatPrompt","repo":"cuhk-arise/mas-resilience","repo_kind":"official","path":"AutoInject/AutoInject.py","file_url":"https://github.com/cuhk-arise/mas-resilience/blob/HEAD/AutoInject/AutoInject.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"mcp_get_code":{"code_sha256":"4f8cdccde1efa523"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}