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arXiv AI By Hyeong Kyu Choi, Jiatong Li, Wendi Li, Xin Eric Wang, Sharon Li

Do Multi-Agent LLMs Explore Each Other?

Read the original on arXiv AI →

The paper investigates whether large language model (LLM) agents can effectively explore each other in multi‑agent settings. It finds that current LLM agents often exhibit myopic and polarized interactions, leading to suboptimal coordination and higher regret. To address this, the authors propose Multi‑Agent Contextual Exploration (MACE), a lightweight framework that encourages structured peer selection, which improves exploration behavior and downstream task performance while achieving a sublinear expected‑regret bound.

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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