LawThinker: A Deep Research Legal Agent in Dynamic Environments
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 .
698e8eb9cace060ff123ac42
Xinyu Yang
claimed_verified
2026-02-13T09:36:50.313Z
6722fe4a43b00f3c889aead4
/avatars/cbb57f517c37c72642b80dd538271238.svg
Xinyu Yang
yxy919
user
yxy919
698e8eb9cace060ff123ac43
Chenlong Deng
claimed_verified
2026-02-13T09:36:47.946Z
654c99d6e82a71cb487c2ecd
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ChenlongDeng
ChenlongDeng
user
ChenlongDeng
698e8eb9cace060ff123ac44
Tongyu Wen
698e8eb9cace060ff123ac45
Binyu Xie
698e8eb9cace060ff123ac46
Zhicheng Dou
LawThinker: A Deep Research Legal Agent in Dynamic Environments
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 .
LawThinker is an autonomous legal research agent that uses an Explore-Verify-Memorize strategy with a DeepVerifier module to ensure accurate and procedurally compliant legal reasoning through dynamic verification of intermediate steps.
legal reasoning autonomous agent Explore-Verify-Memorize strategy DeepVerifier module knowledge accuracy fact-law relevance procedural compliance cross-round knowledge reuse dynamic judicial environments