Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges
Quick summary
arXiv:2607.26212v1 Announce Type: cross Abstract: Multi-Agent Debate (MAD) is a promising paradigm for improving the accuracy and robustness of Large Language Model (LLM)-based agentic systems. It enables multiple agents to exchange arguments, critique each other's outputs, and iteratively converge towards a solution. However, research remains fragmented, with inconsistent terminology and no rigorous synthesis of MAD design dimensions. We present a systematic literature review characterizing 141 primary studies on MAD. We derive a three-dimensional taxonomy covering debate participants, the in
Key takeaways
- arXiv:2607.26212v1 Announce Type: cross Abstract: Multi-Agent Debate (MAD) is a promising paradigm for improving the accuracy and robustness of Large Language Model (LLM)-based agentic systems.
- It enables multiple agents to exchange arguments, critique each other's outputs, and iteratively converge towards a solution.
- However, research remains fragmented, with inconsistent terminology and no rigorous synthesis of MAD design dimensions.
Why it matters
“Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.