LawThinker：動態環境下的深度研究型法律代理

LawThinker: A Deep Research Legal Agent in Dynamic Environments

February 12, 2026

作者: Xinyu Yang, Chenlong Deng, Tongyu Wen, Binyu Xie, Zhicheng Dou

cs.AI

摘要

法律推理不僅要求結果正確，更需要符合程序規範的推理過程。然而現有方法缺乏對中間推理步驟的驗證機制，導致如法條引用不當等錯誤能在推理鏈中未被察覺地傳播。為解決此問題，我們提出LawThinker——一種採用「探索-驗證-記憶」策略的自主法律研究智能體，專為動態司法環境設計。其核心思想是將驗證作為每次知識探索後的原子化操作：DeepVerifier模組從知識準確性、事實與法律關聯性、程序合規性三個維度審查每個檢索結果，並通過記憶模組實現跨輪次知識複用以應對長週期任務。在動態基準J1-EVAL上的實驗表明，LawThinker相比直接推理方法提升24%，較基於工作流的方法提升11%，且在過程導向指標上表現尤為突出。在三個靜態基準上的進一步驗證證實了其泛化能力。代碼已開源於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 .