LLM Agents in Law: Taxonomy, Applications, and Challenges

• URL: http://arxiv.org/abs/2601.06216v1
• Date: Thu, 08 Jan 2026 21:04:35 GMT
• Title: LLM Agents in Law: Taxonomy, Applications, and Challenges
• Authors: Shuang Liu, Ruijia Zhang, Ruoyun Ma, Yujia Deng, Lanyi Zhu, Jiayu Li, Zelong Li, Zhibin Shen, Mengnan Du,
• Abstract summary: Large language models (LLMs) have precipitated a dramatic improvement in the legal domain.<n>The deployment of standalone models faces significant limitations regarding hallucination, outdated information, and verifiability.<n>Recently, LLM agents have attracted significant attention as a solution to these challenges.
• Score: 24.660146939399567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have precipitated a dramatic improvement in the legal domain, yet the deployment of standalone models faces significant limitations regarding hallucination, outdated information, and verifiability. Recently, LLM agents have attracted significant attention as a solution to these challenges, utilizing advanced capabilities such as planning, memory, and tool usage to meet the rigorous standards of legal practice. In this paper, we present a comprehensive survey of LLM agents for legal tasks, analyzing how these architectures bridge the gap between technical capabilities and domain-specific needs. Our major contributions include: (1) systematically analyzing the technical transition from standard legal LLMs to legal agents; (2) presenting a structured taxonomy of current agent applications across distinct legal practice areas; (3) discussing evaluation methodologies specifically for agentic performance in law; and (4) identifying open challenges and outlining future directions for developing robust and autonomous legal assistants.

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