Investigating Multi-Agent Deliberation in Law
A new study explores multi-agent deliberation methods for legal reasoning using LLMs, introducing two novel frameworks inspired by courtroom procedures and legal argumentation. Experiments show comparable overall performance to single models but significantly distinct answers, with multi-agent approaches excelling in tasks requiring critical thinking from multiple perspectives.
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[Submitted on 29 Jun 2026]
Title:Investigating Multi-Agent Deliberation in Law
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Abstract:Artificial Intelligence is increasingly applied to the field of law, and has the potential to increase access to justice. One particular movement that is gaining traction is that of agentic AI, wherein AI agents, based on Large Language Models (LLMs) can take autonomous actions. In particular, multi-agent approaches in the legal domain remain largely unexplored. In this paper, we investigate multi-agent deliberation methods for legal reasoning tasks using LLMs. We explore multi-agent deliberation (MAD) and introduce two novel multi-agent frameworks inspired by courtroom procedures and legal argumentation. Our experiments on both legal and non-legal benchmarks reveal that multi-agent frameworks achieve comparable overall performance to baseline large language models, but produce significantly distinct answers. Notably, these approaches can successfully solve cases that the baseline fails to address, and vice versa. We conduct a qualitative evaluation and highlight scenarios where multi-agent frameworks outperform monolithic approaches. For example, multi-agent approaches appear better suited for answering questions that require critical thinking from multiple perspectives. Our work positions multi-agent systems as a promising direction for AI in the legal domain, while demonstrating the potential of law-inspired multi-agent approaches for deliberation.
Comments: This manuscript has been accepted for presentation at the AIDA2J Workshop during the 21st International Conference of AI & Law in Singapore, June 8 2026
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.30906 [cs.AI]
(or arXiv:2606.30906v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.30906
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Cor Steging [view email] [v1] Mon, 29 Jun 2026 20:56:37 UTC (1,039 KB)
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