Improving Multi-Agent Debate with Sparse Communication Topology (2024.findings-emnlp)
Copied to clipboard
| 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. |
Similar Papers
Voting or Consensus? Decision-Making in Multi-Agent Debate (2025.findings-acl)
Copied to clipboard
| Challenge: | Increasing the number of agents improves performance, while more discussion rounds before voting reduces it. |
| Approach: | They propose two new methods to improve multi-agent debates by increasing agent diversity and reducing discussion rounds before voting. |
| Outcome: | The proposed methods improve task performance by up to 3.3% with AAD and up to 7.4% with CI. |
Debate to Align: Reliable Entity Alignment through Two-Stage Multi-Agent Debate (2026.findings-acl)
Copied to clipboard
| Challenge: | Entity alignment (EA) aims to identify entities referring to the same real-world object across different knowledge graphs (KGs). |
| Approach: | They propose a reliable EA framework based on multi-agent debate that improves embedding quality and introduces a two-stage multi-role debate mechanism to enhance reliability. |
| Outcome: | The proposed framework improves embedding quality and the reasoning capability of LLMs while enabling more efficient debate-based reasoning. |
Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key? (2024.acl-long)
Copied to clipboard
| Challenge: | Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLM. |
| Approach: | They propose a group discussion framework to enrich the set of discussion mechanisms. |
| Outcome: | The proposed framework performs better on a wide range of reasoning tasks and backbone LLMs. |
Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems (2025.emnlp-main)
Copied to clipboard
| Challenge: | Empirical studies for communication topology design often overlook why and when sparse and dense topologies help or hinder collaboration. |
| Approach: | They propose a topology design approach that balances error suppression and beneficial information propagation by fusing connectivity patterns from dense and sparse graphs. |
| Outcome: | The proposed topology design achieves superior performance across tasks with sparse and dense graphs. |
Beyond Frameworks: Unpacking Collaboration Strategies in Multi-Agent Systems (2025.acl-long)
Copied to clipboard
| Challenge: | Existing frameworks prioritize structural architectures and role assignments but neglect granular mechanics of agent collaboration. |
| Approach: | They propose to use centralized governance, instructor-led participation, ordered interaction patterns to optimize task accuracy and computational efficiency. |
| Outcome: | The proposed model improves task accuracy and computational efficiency under two context-dependent scenarios. |
CortexDebate: Debating Sparsely and Equally for Multi-Agent Debate (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to improve the reasoning performance of LLMs suffer from two major shortcomings: too lengthy input contexts and overconfidence dilemma. |
| Approach: | They propose a method to debating among LLM agents using a sparse debator graph . they use a module called McKinsey-based Debate Matter to optimize the debators . |
| Outcome: | The proposed method has been well demonstrated across eight datasets from four task types. |
S2-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency (2025.naacl-long)
Copied to clipboard
Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, XiTai Jin, Chen Tianying Tiana, Jing Li, Xiaohua Xu
| Challenge: | Large language models exhibit limitations when handling complex mathematical reasoning and logical inference tasks. |
| Approach: | They propose a sparsification strategy to reduce token costs within Multi-agent Debate (MAD) this strategy minimizes ineffective exchanges of information and unproductive discussions among agents . |
| Outcome: | The proposed approach reduces token costs by up to 94.5% while maintaining performance degradation below 2.0%. |
Demystifying Multi-Agent Debate: The Role of Confidence and Diversity (2026.findings-acl)
Copied to clipboard
| 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. |
Latent Agents: A Post-Training Procedure for Internalized Multi-Agent Debate (2026.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |