Story · arXiv
The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams (arXiv)
paper · Story page
Under matched budgets on 11 verifier-scored optimization tasks, agents that read each other's complete outputs converged within one round and lost the diversity that justified running several models. Independent proposals were the stronger default; critique helped only when the violated rule was straightforward to find and fix.
In plain words
- Researchers found that sharing complete answers made different artificial intelligence systems follow the first approach and lose useful variety.
- Across 11 scored problem-solving tasks, the systems became similar within one round when they read one another's full answers.
- Having each system propose an answer independently avoided that loss of variety.
- Feedback helped mainly when a broken rule was easy for the software to spot and fix.
- Teams using several software assistants may get better choices by collecting separate answers before allowing discussion.
Appeared in
- Compaction erases agent safety rules, and CLAUDE.md prose isn't a control
Aug 26, 2026 · in the sections
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