The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams
Score
75首次系统性提出“交互税”概念，梳理并调和了多智能体研究中矛盾的结论。
实验设计严谨，覆盖11个任务和多种交互模式，论证链条完整。
为多智能体框架的工程实现提供直接启示：应独立生成或精细控制信息共享。
紧贴当前多智能体LLM应用热点，回应了近期关于交互收益的争论。
来自arXiv预印本，引用和实验较规范，但尚未经同行评审。
Base 83 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
The paper uses solid experiments and controlled comparisons to reveal the central role of interaction, offering strong academic and practical guidance.
AI commentary
「This paper is a must-read for anyone building multi-agent systems. It uses a simple but profound experiment to puncture the intuition that more interaction equals better results. For engineers designing agent frameworks, the takeaway is direct: sample independently first, then decide whether to interact, and be cautious about letting agents read each other's full answers.」
Quick overview
Does multi-agent LLM interaction help or hurt? This paper argues that contradictory findings stem from a missing distinction: not all multi-agent communication is equal. The authors show that different model families find structurally different solutions, but when agents read each other's complete outputs, their proposals converge within one round, erasing the diversity that motivates using multiple models—termed the interaction tax. Testing 11 verifier-scored optimization tasks under matched budgets, they find full-solution interaction is a weak default, while independent proposal generation avoids collapse. Critique helps only when the violated rule is easy for the LLM to find and fix. Performance depends less on the number of agents than on the information exchanged.
- Introduces the 'interaction tax': full-solution interaction causes multi-agent proposals to converge within one round, erasing diversity.
- Across 11 verifier-scored tasks, full-solution interaction is a weak default under matched budgets compared to independent generation.
- Critique helps only when the violated rule is easy for the LLM to find and fix.
- Multi-agent performance depends less on the number of agents than on the type and timing of information exchanged.
Full text
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