LRAS: 基于代理搜索的先进法律推理
LRAS: Advanced Legal Reasoning with Agentic Search
AI总结：
LRAS通过整合反思模仿学习和难度感知强化学习，使大推理模型能够识别知识边界并处理法律推理的复杂性，从而在法律领域取得显著性能提升。
摘要：
尽管大推理模型（LRMs）在数学领域展示了卓越的逻辑能力，但其在法律领域的应用受到严格的程序严谨性和法律逻辑要求的限制。现有的法律大语言模型（LLMs），依赖于仅从内部参数知识中推导出的
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
