# LexKairos: Benchmarking Legal Temporal Capabilities in LLMs

arXiv: https://arxiv.org/abs/2608.09106 · HTML: https://mlsanity.com/paper/2608.09106/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Builds on (most cited first, 13 of 13)

- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [Legal Syllogism Prompting: Teaching Large Language Models for Legal Judgment Prediction](https://mlsanity.com/paper/2307.08321/) (2023)
- [Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time](https://mlsanity.com/paper/2409.13338/) (2024)
- [LexTime: A Benchmark for Temporal Ordering of Legal Events](https://mlsanity.com/paper/2506.04041/) (2025)
- [The CLC-UKET Dataset: Benchmarking Case Outcome Prediction for the UK Employment Tribunal](https://mlsanity.com/paper/2409.08098/) (2024)
- [LegalOne: A Family of Foundation Models for Reliable Legal Reasoning](https://mlsanity.com/paper/2602.00642/) (2026)
- [LexGenius: An Expert-Level Benchmark for Large Language Models in Legal General Intelligence](https://mlsanity.com/paper/2512.04578/) (2025)
- [LexChain: Modeling Legal Reasoning Chains for Chinese Tort Case Analysis](https://mlsanity.com/paper/2510.17602/) (2025)
- [CLaw: Benchmarking Chinese Legal Knowledge in Large Language Models - A Fine-grained Corpus and Reasoning Analysis](https://mlsanity.com/paper/2509.21208/) (2025)
- [LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases](https://mlsanity.com/paper/2512.12643/) (2025)
- [LexChronos: An Agentic Framework for Structured Event Timeline Extraction in Indian Jurisprudence](https://mlsanity.com/paper/2603.01651/) (2026)

## Abstract (arXiv)

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.