Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems

Excerpt

arXiv:2605.05703v3 Announce Type: replace-cross Abstract: 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