The Interaction Tax: Letting Multi-Agent LLMs Read Each Other's Full Outputs Collapses Solution Diversity Within One Round

arXiv 2608.23541·high signalXBlueskyLinkedInCopy link

Testing 11 verifier-scored optimization tasks under matched compute budgets, the authors find that different model families genuinely find structurally different solutions, but when agents read each other's complete outputs their proposals converge in a single round, erasing exactly the diversity that motivated using multiple models. They call full-solution interaction a weak default and argue it mainly anchors agents to the first solution they see rather than exploring alternatives; critique loops help only when the violated rule is easy for an LLM to find and fix. Independent proposal generation avoids the collapse, which reframes the contradictory prior literature on debate and mixture-of-agents as a question about what information gets exchanged, not how many agents there are.