L-MAD: A Systematic Evaluation of Multi-Agent Debate Structures in Legal Reasoning
The L-MAD framework systematically evaluates multi-agent debate structures and aggregation methods in Legal Textual Entailment. By assigning expert personas, it improves upon single-agent baselines by up to 8%. Increasing agent population reduces inconsistency and improves accuracy, but extending discussion rounds induces over-deliberation drift where agents reinforce each other's mistakes. The findings outline practical boundaries for deploying multi-agent systems in high-stakes legal reasoning.
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[Submitted on 10 Jul 2026]
Title:L-MAD: A Systematic Evaluation of Multi-Agent Debate Structures in Legal Reasoning
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Abstract:While multi-agent debate (MAD) frameworks have shown significant potential in general reasoning, their effectiveness in highly structured, knowledge-heavy legal domains remains under-explored. In this work, we introduce the Legal Multi-Agent Debate (L-MAD) framework to systematically evaluate different debate structures and aggregation methods within Legal Textual Entailment. By assigning distinct expert personas to multiple agents, L-MAD improves upon strong single-agent baselines by up to 8\%. Furthermore, analyzing how debate scales reveals a clear trade-off: increasing the agent population reduces inconsistency and improves accuracy, whereas extending discussion rounds induces a detrimental \textit{over-deliberation drift} where agents reinforce each other's mistakes. Ultimately, our findings outline the practical boundaries and safety margins of deploying collaborative multi-agent systems in high-stakes legal reasoning environments.
Comments: Outstanding paper in the AI4Law Workshop at ICML 2026
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.09099 [cs.AI]
(or arXiv:2607.09099v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.09099
arXiv-issued DOI via DataCite (pending registration)
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From: Tan-Minh Nguyen [view email] [v1] Fri, 10 Jul 2026 05:08:02 UTC (1,185 KB)
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