法律思想家：动态环境下的深度研究型法律智能体

摘要

法律推理不仅要求结果正确，更需要遵循程序合规的推理过程。然而现有方法缺乏对中间推理步骤的验证机制，导致诸如法条引用不当等错误能在推理链中未被察觉地传播。为此，我们提出LawThinker——一种面向动态司法环境的自主法律研究智能体，采用“探索-验证-记忆”策略。其核心思想是将验证作为每次知识探索后的原子化操作：通过DeepVerifier模块从知识准确性、事实与法律关联性、程序合规性三个维度检验每次检索结果，并配备记忆模块实现长周期任务中的跨轮次知识复用。在动态基准J1-EVAL上的实验表明，LawThinker较直接推理方法提升24%，较基于工作流的方法提升11%，且在过程导向指标上表现尤为突出。在三个静态基准上的评估进一步验证了其泛化能力。代码已开源：https://github.com/yxy-919/LawThinker-agent。

English

Legal reasoning requires not only correct outcomes but also procedurally compliant reasoning processes. However, existing methods lack mechanisms to verify intermediate reasoning steps, allowing errors such as inapplicable statute citations to propagate undetected through the reasoning chain. To address this, we propose LawThinker, an autonomous legal research agent that adopts an Explore-Verify-Memorize strategy for dynamic judicial environments. The core idea is to enforce verification as an atomic operation after every knowledge exploration step. A DeepVerifier module examines each retrieval result along three dimensions of knowledge accuracy, fact-law relevance, and procedural compliance, with a memory module for cross-round knowledge reuse in long-horizon tasks. Experiments on the dynamic benchmark J1-EVAL show that LawThinker achieves a 24% improvement over direct reasoning and an 11% gain over workflow-based methods, with particularly strong improvements on process-oriented metrics. Evaluations on three static benchmarks further confirm its generalization capability. The code is available at https://github.com/yxy-919/LawThinker-agent .

法律思想家：动态环境下的深度研究型法律智能体

LawThinker: A Deep Research Legal Agent in Dynamic Environments

摘要

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