Papers with Multi-agent debate
MALLM: Multi-Agent Large Language Models Framework (2025.emnlp-demos)
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| Challenge: | Multi-agent debate (MAD) has demonstrated the ability to augment collective intelligence by scaling test-time compute and leveraging expertise. |
| Approach: | They propose an open-source framework that enables systematic analysis of multi-agent debates. |
| Outcome: | The proposed framework enables systematic analysis of multi-agent debate components. |
Stay Focused: Problem Drift in Multi-Agent Debate (2026.findings-eacl)
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| Challenge: | Multi-agent debates have shown promise for solving knowledge and reasoning tasks, but they are limited when solving complex problems that require longer reasoning chains. |
| Approach: | They propose a method to detect problem drift and propose 'driFTJudge' which mitigates 31% of problem drift cases. |
| Outcome: | The proposed method mitigates 31% of problem drift cases and is based on a set of ten tasks across ten different tasks. |
Improving Multi-Agent Debate with Sparse Communication Topology (2024.findings-emnlp)
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| Challenge: | Existing approaches to multi-agent debates use a brute force algorithm, resulting in a computationally intensive process. |
| Approach: | They propose to extend the multi-agent debate framework to multi-modal reasoning and alignment labeling tasks, showcasing its broad applicability and effectiveness. |
| Outcome: | The proposed framework can achieve comparable or superior performance while significantly reducing computational costs. |
When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning (2026.acl-long)
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| Challenge: | Multi-agent debate (MAD) aims to improve large language model reasoning by letting multiple agents exchange answers and then aggregate their opinions. |
| Approach: | They propose a principled framework that joins sycophancy and self-bias to mitigate and quantify identity bias in multi-agent debate by removing identity markers from prompts. |
| Outcome: | The proposed framework joins identity-driven sycophancy and self-bias to mitigate and quantify identity bias in multi-agent debate. |
Latent Agents: A Post-Training Procedure for Internalized Multi-Agent Debate (2026.acl-long)
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| Challenge: | Multi-agent debate is compute-intensive and requires long transcripts before answering questions. |
| Approach: | They propose a framework that distills multi-agent debate into a single LLM by combining debate structure learning with internalization via dynamic reward scheduling and length clipping. |
| Outcome: | The proposed model matches or exceeds explicit multi-agent debate performance using 93% fewer tokens across multiple models and benchmarks. |
Free-MAD: Consensus-Free Multi-Agent Debate (2026.findings-acl)
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| Challenge: | Existing multi-agent debate methods rely on multiple rounds of interaction among agents to reach consensus, and the final output is decided by majority voting in the last round. |
| Approach: | They propose a multi-agent debate framework that eliminates the need for consensus among agents and reconstructs the debate phase by introducing anti-conformity. |
| Outcome: | Experiments on eight benchmark datasets show that Free-MAD significantly improves reasoning performance while requiring only a single-round debate and thus reducing token costs. |
Demystifying Multi-Agent Debate: The Role of Confidence and Diversity (2026.findings-acl)
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| Challenge: | Multi-agent debate (MAD) is widely used to improve large language models' (LLMs) reasoning and test-time scaling. |
| Approach: | They propose a diversity-aware initialisation that selects a more diverse pool of candidate answers, increasing the likelihood that a correct hypothesis is present at the start of debate. |
| Outcome: | The proposed protocol outperforms vanilla MAD and majority vote on six reasoning-oriented QA benchmarks. |