Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems
Optimizing the communication structure of large language model based multi-agent systems (LLM-MAS) has been shown to improve downstream performance and reduce token usage. Existing methods typically rely on randomly sampled training tasks. However, tasks may differ substantially in difficulty and domain, and thus they are not equally informative for updating communication structure, making optimization often unstable and highly sensitive to the particular training set. To actively identify the most valuable tasks for communication-structure optimization, we propose an ensemble-based information-theoretic task selection framework. The proposed method estimates task informativeness by how m...
| Authors | Huchen Yang, Xinghao Dong, Dan Negrut et al. |
|---|---|
| Primary category | cs.MA |
| Published | 7 May 2026 |
| Last updated at source | 2 October 2026 |
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