Hear Both Sides: Efficient Multi-Agent Debate via Diversity-Aware Message Retention
Multi-Agent Debate has emerged as a promising framework for improving the reasoning quality of large language models through iterative inter-agent communication. However, broadcasting all agent messages at every round introduces noise and redundancy that can degrade debate quality and waste computational resources. Current approaches rely on uncertainty estimation to filter low-confidence responses before broadcasting, but this approach is unreliable due to miscalibrated confidence scores and sensitivity to threshold selection. To address this, we propose Diversity-Aware Retention (DAR), a lightweight debate framework that, at each debate round, selects the subset of agent responses that ...
| Authors | Manh Nguyen, Anh Nguyen, Dung Nguyen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 March 2026 |
| Last updated at source | 14 April 2026 |
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