Improving Role Consistency in Multi-Agent Collaboration via Quantitative Role Clarity

Computer Science > Artificial Intelligence [Submitted on 3 Apr 2026] Improving Role Consistency in Multi-Agent Collaboration via Quantitative Role Clarity Guoling Zhou, Wenpei Han, Fengqin Yang, Li Wang, Yingcong Zhou, Zhiguo Fu In large language model (LLM)-driven multi-agent systems, disobey role specification (failure to adhere to the defined responsibilities and constraints of an assigned role, potentially leading to an agent behaving like another) is a major failure mode \cite{DBLP:journals/corr/abs-2503-13657}. To address this issue, in the present paper, we propose a quantitative role clarity to improve role consistency. Firstly, we construct a role assignment matrix S(\phi)=[s_{ij}(\phi)], where s_{ij}(\phi) is the semantic similarity between the i-th agent's behavior trajectory and the j-th agent's role description. Then we define role clarity matrix M(\phi) as \text{softmax}(S(\phi))-I, where \text{softmax}(S(\phi)) is a row-wise softmax of S(\phi) and I is the identity matrix. The Frobenius norm of M(\phi) quantifies the alignment between agents' role descriptions and their behaviors trajectory. Moreover, we employ the role clarity matrix as a regularizer during lightweight fine-tuning to improve role consistency, thereby improving end-to-end task performance. Experiments on the ChatDev multi-agent system show that our method substantially improves role consistency and task performance: with Qwen and Llama, the role overstepping rate decreases from 46.4\% to 8.4\% and from 43.4\% to 0.2\%, respectively, and the role clarity score increases from 0.5328 to 0.9097 and from 0.5007 to 0.8530, respectively, the task success rate increases from 0.6769 to 0.6909 and from 0.6174 to 0.6763, respectively. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.02770 [cs.AI]   (or arXiv:2604.02770v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.02770 Focus to learn more Submission history From: Guoling Zhou [view email] [v1] Fri, 3 Apr 2026 06:28:59 UTC (259 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)