法律 AI // 资讯总览
需要被持续追踪的
法律 AI 信号。
本期索引按监管、判例、实务与研究归档；可依主题或关键字定位已发布记录。
法律 AI // 资讯总览
本期索引按监管、判例、实务与研究归档；可依主题或关键字定位已发布记录。
原题: LexKairos: Benchmarking Legal Temporal Capabilities in LLMs
涉及人工智能算法备案、生成式模型合规红线与法律科技核心准则。
arXiv论文《LexKairos: Benchmarking Legal Temporal Capabilities in LLMs》提出一项面向中国法律语境的大模型法律时间能力基准测试。该基准从法条时间知识、案件时间建模、法条与案件时间推理三个维度展开，包含基于真实中国司法案件和法律条文构建的九项子任务。研究团队在普通推理、思维链（CoT）和思考模式等多种推理设置下，对八个大语言模型进行了系统评测。结果显示，Gemini-3-Flash整体表现最佳，但即使是该模型，在精确召回具有时间敏感性的法条元数据，以及处理复杂期限推理任务时仍存在明显局限。论文同时公布了数据和代码，地址为https://github.com/thunlp/LexKairos。
展示来源全文，完整内容以原文页面为准。
arXiv:2608.09106v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong performance across a wide range of legal tasks. In legal practice, time is a critical concept that governs the validity of statutes, the progression of legal cases, and the enforcement of procedural deadlines. However, legal temporal capabilities remain underexplored in existing legal AI benchmarks. To address this gap, we propose LexKairos, a comprehensive benchmark for evaluating the temporal capabilities of LLMs in the Chinese legal context across three dimensions: statutory temporal knowledge, case temporal modeling, and statute-case temporal reasoning. LexKairos comprises nine sub-tasks drawn from real-world Chinese judicial cases and statutes. We conduct systematic evaluations of eight LLMs under multiple inference settings, including vanilla, Chain-of-Thought (CoT), and thinking modes. Our results show that Gemini-3-Flash achieves the strongest overall performance, yet even the best-performing model exhibits notable limitations on tasks demanding precise time-sensitive statutory metadata recall or complex reasoning in time limits, indicating that legal temporal knowledge and reasoning remain open challenges for current LLMs. Data and code are available at https://github.com/thunlp/LexKairos.
文书与研报规范引注
《LexKairos评测大模型法律时间能力》（来源: arXiv cs.CL，发布日期: 2026/10/12，访问链接: https://arxiv.org/abs/2608.09106）