The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams

TL;DR - This paper identifies an “interaction tax” in multi-agent LLM teams: sharing complete solutions can rapidly homogenize agents’ proposals and eliminate the diversity that makes multiple models useful. Across 11 verifier-scored optimization tasks with matched budgets, independent generation was a stronger default than full-solution interaction.

• Agents exposed to complete peer outputs converged within one round and tended to remain close to the first solution seen.
• Independent proposal generation preserved structurally different approaches across model families.
• Critique improved results only when the violated constraint was easy for the LLM to identify and repair.
• Performance depended more on what information agents exchanged—and when—than on the number of agents.