@inproceedings{deng-etal-2026-adaptime,
    title = "{A}dap{T}ime: Enabling Adaptive Temporal Reasoning in Large Language Models",
    author = "Deng, Yimin  and
      Wang, Yejing  and
      Lin, Zhenxi  and
      Fu, Zichuan  and
      Zhao, Guoshuai  and
      Xu, Derong  and
      Zheng, Yefeng  and
      Zhao, Xiangyu  and
      Wu, Xian  and
      Zhu, Li  and
      Qian, Xueming",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1621/",
    pages = "32394--32409",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models have demonstrated strong reasoning capabilities in general knowledge question answering. However, their ability to handle temporal information remains limited. To address this limitation, existing approaches often design time-sensitive reasoning pipelines that rely on external tools or manual verification and are tailored to specific scenarios, leading to poor generalizability. Moreover, these methods apply a fixed pipeline to all questions, overlooking the fact that different types of temporal questions often require distinct reasoning strategies, which leads to unnecessary processing for simple cases and inadequate reasoning for more complex ones. To this end, we propose AdapTime, an adaptive temporal reasoning method that dynamically executes reasoning steps based on the input context and task requirements. Specifically, it involves three temporal reasoning actions: reformulate, rewrite and review, with an LLM planner guiding the reasoning process. AdapTime integrates seamlessly with state-of-the-art LLMs and significantly enhances their temporal reasoning capabilities without relying on external support. Extensive experiments on two temporal QA benchmarks demonstrate the effectiveness of our approach."
}