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Testing Interchangeability in LLM Agent Teams

arXiv cs.AI LLM Agents Jianxin Gao, Tianyi Yu, Linna Deng, Runze Li, Zining Wang 2026-09-04

TL;DR - This study tests whether role-matched agents can be exchanged between established LLM teams without harming performance. Swaps barely affect task scores but increase communication per unit of progress by 16–63%, showing that learned team conventions make agents less interchangeable than outcomes alone suggest.

  • Eight independently formed teams retained private notebooks over ten formation episodes before agents were swapped and evaluated on held-out tasks.
  • In Hanabi, a swapped agent was costlier than an inexperienced agent, consistent with interference from conventions learned with its previous partner.
  • In Collab-Overcooked, replacing the agenda-setting agent caused most additional communication to come from the teammate who remained.
  • Swap penalties tracked how far independently formed teams diverged: greedy decoding reduced both, while longer team histories increased both.

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