让法律大模型像律师一样主动查资料，避免自以为是地瞎猜。
LRAS: Advanced Legal Reasoning with Agentic Search
- 用自我反思和难度感知强化学习，让模型知道什么不会、该去查。
- 在复杂法律推理任务上比顶尖模型提升8.2%至32%。
- 适合需要严谨推理的法律AI研究者或司法科技开发者。
尽管大型推理模型（LRMs）在数学领域展现出卓越的逻辑能力，但在法律领域的应用仍受限于程序严谨性与法律逻辑的严格要求。现有法律大模型依赖仅来自内部参数知识的“闭合回路推理”，常因缺乏对知识边界的自知而做出自信却错误的结论。为此，我们提出法律推理的智能体搜索框架（LRAS），首次将法律大模型从静态的“闭合回路思维”转向动态的“主动探询”。通过引入内省模仿学习与难度感知强化学习，LRAS使模型能识别知识边界并应对法律推理复杂性。实验证明，LRAS在性能上超越最先进基线8.2%至32%，尤其在需深度推理与可靠知识的任务中表现突出。数据集与模型即将开源，供进一步研究。
原文摘要 · Abstract (English)
While Large Reasoning Models (LRMs) have demonstrated exceptional logical capabilities in mathematical domains, their application to the legal field remains hindered by the strict requirements for procedural rigor and adherence to legal logic. Existing legal LLMs, which rely on "closed-loop reasoning" derived solely from internal parametric knowledge, frequently suffer from lack of self-awareness regarding their knowledge boundaries, leading to confident yet incorrect conclusions. To address this challenge, we present Legal Reasoning with Agentic Search (LRAS), the first framework designed to transition legal LLMs from static and parametric "closed-loop thinking" to dynamic and interactive "Active Inquiry". By integrating Introspective Imitation Learning and Difficulty-aware Reinforcement Learning, LRAS enables LRMs to identify knowledge boundaries and handle legal reasoning complexity. Empirical results demonstrate that LRAS outperforms state-of-the-art baselines by 8.2-32\%, with the most substantial gains observed in tasks requiring deep reasoning with reliable knowledge. We will release our data and models for further exploration soon.
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