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arXiv AI By Songmiao Wang, Yuchen Wang, Chenglin Yu, Hongxia Yang, Ming Li

Competence-Aware Orchestration for LLM-Based Multi-Agent Systems

Read the original on arXiv AI →

The paper introduces a two-stage competence-aware framework for orchestrating large language model (LLM)-based multi-agent systems (MAS). It first estimates the LLM backbone’s task-conditioned competence for a given query, then uses this estimate to adjust which roles are activated and how agents communicate. Across six benchmarks, the method improves average performance to 93.81% and, depending on backbone competence, can boost accuracy while reducing token costs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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