法律推理中引入动态验证机制，防止错误信息传播。
LawThinker: A Deep Research Legal Agent in Dynamic Environments
- 每步检索后强制执行三维度验证，确保法律推理准确
- 在动态测试集上比直接推理提升24%，流程指标显著优化
- 适合需要高合规性的法律自动化场景，如司法辅助系统
法律推理不仅要求正确结果，还需符合程序规范。现有方法缺乏对中间推理步骤的验证机制，导致不适用法条引用等错误可在推理链中持续传播。为此，我们提出 LawThinker，一个面向动态司法环境的自主法律研究代理，采用“探索-验证-记忆”策略。核心思想是在每次知识探索后将验证作为原子操作执行。DeepVerifier 模块从知识准确性、事实-法律相关性、程序合规性三个维度审查每项检索结果，并通过记忆模块实现跨轮次知识复用，支持长周期任务。在动态基准测试 J1-EVAL 上，LawThinker 相较于直接推理提升 24%，较工作流方法提升 11%，尤其在流程导向指标上表现突出。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。
原文摘要 · Abstract (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 .
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