arXiv：cs.AI· Haotian Chen, Shuaicheng Niu, Haocong Rao, Kaisong Song, Jun Lin, Lizhen Cui, Zhiqi Shen, Yonghui Xu·· 5 小时前AI 评分33
面向长程法律推理的测试时智能体演化
Test-Time Agent Evolution for Long-Horizon Legal Reasoning
AI 导读
研究者提出测试时智能体演化（Test-Time Agent Evolution）方法，通过"测试时记忆演化"从既往案例中检索可复用经验并适配当前情境，"评分标准对齐协作"按行为与流程要求校验各角色动作。在 J1-EVAL 和 LegalWorld 上、五种骨干模型的实验中，该方法相比代表性推理与智能体基线取得一致提升，交互与计算成本合理，且无需更新模型参数。
正文
Abstract:Legal intelligence aims to support reliable decision-making across long-horizon legal processes involving evolving case states and multiple roles. However, real-world legal deployment exhibits substantial case heterogeneity in facts, evidence, and procedural contexts, exposing the limitations of static agent strategies. Moreover, legal reasoning is inherently interdependent across roles and procedural stages, making global reliability fundamentally different from isolated role competence. To address these challenges, we study training-free test-time agent adaptation, where agents continuously exploit deployment-time signals from preceding cases and ongoing interactions without updating model parameters. We propose \method, which introduces \emph{Test-Time Memory Evolution} to retrieve reusable experience from previous cases, adapt it to the current factual and procedural context, and consolidate accumulated experience for subsequent decision-making. Further, \emph{Rubric-Aligned Collaboration} verifies and revises role-specific actions according to behavioral and procedural requirements, enabling coordinated decision-making across roles and stages. Extensive experiments on J1-EVAL and LegalWorld across five backbone models demonstrate consistent improvements over representative reasoning and agent baselines with reasonable interaction and computational costs. Ablation and case studies further show that the two components provide complementary benefits in experience adaptation and cross-role coordination, improving the reliability and efficiency of long-horizon legal reasoning.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08138 [cs.AI] |
| (or arXiv:2610.08138v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08138 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Haotian Chen [view email]
[v1]
Tue, 6 Oct 2026 10:53:25 UTC (893 KB)
来源：arXiv：cs.AI · arxiv.org