LLM法律代理能更好处理法律任务，减少幻觉和信息过时问题。
LLM Agents in Law: Taxonomy, Applications, and Challenges
- 用规划、记忆和工具调用提升法律任务处理能力
- 系统梳理法律领域代理应用的分类与技术演进
- 适合法律AI研究者和智能法律系统开发者参考
大型语言模型（LLMs）在法律领域带来显著进步，但独立部署存在幻觉、信息过时和可验证性差等局限。最近，基于规划、记忆和工具调用的LLM代理成为解决这些问题的方案，更符合法律实践的严谨要求。本文全面综述法律领域的LLM代理，分析其如何弥合技术能力与法律需求之间的差距。主要贡献包括：（1）系统梳理从通用法律LLM到法律代理的技术演进；（2）构建法律代理在不同法律实践领域的结构化分类体系；（3）讨论法律场景下代理性能的评估方法；（4）识别现存挑战并提出未来发展方向，推动可靠且自主的法律助理系统发展。
原文摘要 · Abstract (English)
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in the legal domain, yet the deployment of standalone models faces significant limitations regarding hallucination, outdated information, and verifiability.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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