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arXiv AI By Jianxin Gao, Runze Li, Tianyi Yu, Liangwei Ren, Bohan Chen, Zining Wang

Interchangeable in Role, Not in History: Replacing Members of Formed LLM Teams

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The study examines how swapping members in large‑language‑model (LLM) teams affects performance. By replacing a team member with a newcomer who either carries the original team’s written notebook, a notebook from another team, or no notebook, the authors find that newcomers with the original notebook often outperform those without it in cooperative tasks such as cooking, wildfire response, and medical Q&A. However, when compared to the original team, these replacements consistently underperform and require more messages per unit of progress, with the deficit persisting across model families and decoding temperatures and narrowing only when planning‑only members are added.

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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