Hear Both Sides: Efficient Multi-Agent Debate via Diversity-Aware Message Retention
arXiv:2603.20640v1 Announce Type: new Abstract: 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 maximally disagree with each other and with the majority vote before broadcasting. Through an explicit index-based retention mechanism, DAR preserves the original messages without modification, ensuring that retained disagreements remain authentic. Exper
arXiv:2603.20640v1 Announce Type: new Abstract: 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 maximally disagree with each other and with the majority vote before broadcasting. Through an explicit index-based retention mechanism, DAR preserves the original messages without modification, ensuring that retained disagreements remain authentic. Experiments on diverse reasoning and question answering benchmarks demonstrate that our selective message propagation consistently improves debate performance, particularly as the number of agents scales, where noise accumulation is most severe. Our results highlight that what agents hear is as important as what agents say in multi-agent reasoning systems.
Executive Summary
This article proposes Diversity-Aware Retention (DAR), a novel debate framework that selectively retains agent responses to maximize disagreement and improve debate performance in multi-agent reasoning systems. DAR's index-based retention mechanism preserves the authenticity of retained disagreements, addressing the limitations of uncertainty estimation-based approaches. The authors demonstrate the efficacy of DAR on diverse benchmarks, highlighting its potential to enhance debate quality and efficiency. The results underscore the importance of selective message propagation in multi-agent reasoning systems, particularly as the number of agents increases.
Key Points
- ▸ Diversity-Aware Retention (DAR) selectively retains agent responses to maximize disagreement
- ▸ DAR's index-based retention mechanism preserves the authenticity of retained disagreements
- ▸ DAR improves debate performance on diverse reasoning and question-answering benchmarks
Merits
Strengths in Addressing Uncertainty Estimation Limitations
DAR offers a more reliable approach to filtering low-confidence responses by leveraging disagreement-based retention, which is less susceptible to miscalibrated confidence scores and threshold selection.
Demerits
Potential Overreliance on Disagreement-Based Retention
DAR's performance may be compromised if the retained disagreements are not representative of the underlying debate dynamics, potentially leading to noise accumulation and decreased debate quality.
Expert Commentary
DAR's innovative approach to selective message propagation demonstrates a nuanced understanding of the complexities involved in multi-agent reasoning systems. However, the study's limitations, such as potential overreliance on disagreement-based retention, warrant further investigation to ensure the robustness and scalability of DAR in real-world applications. As the field of multi-agent reasoning continues to evolve, the study's findings and DAR's framework will undoubtedly contribute to a more comprehensive understanding of the role of selective message propagation in enhancing debate quality and efficiency.
Recommendations
- ✓ Future research should investigate the robustness and scalability of DAR in diverse real-world applications.
- ✓ Developing more effective debate frameworks and policies that integrate DAR's selective message propagation mechanism may inform the development of more efficient and effective multi-agent reasoning systems.
Sources
Original: arXiv - cs.CL
