@inproceedings{cheng-etal-2026-llm,
    title = "Your {LLM} Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception",
    author = "Cheng, Yize  and
      Moakhar, Arshia Soltani  and
      Fan, Chenrui  and
      Hosseini, Parsa  and
      Faghih, Kazem  and
      Sodagar, Zahra  and
      Wang, Wenxiao  and
      Feizi, Soheil",
    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.1848/",
    pages = "37082--37104",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language model (LLM) agents are increasingly used to interact with and execute tasks in dynamic environments. However, a critical yet overlooked limitation of these agents is that they, by default, assume a stationary context, failing to account for the real-world time elapsed between messages. We refer to this as ``temporal blindness''. This limitation hinders decisions about when to invoke tools, leading agents to either over-rely on stale context and skip needed tool calls, or under-rely on it and redundantly repeat tool calls. To study this challenge, we constructed TicToc, a diverse dataset of multi-turn user{--}agent message trajectories across 76 scenarios, spanning dynamic environments with high, medium, and low time sensitivity. We collected human preferences between ``calling a tool'' and ``directly answering'' on each sample, and evaluated how well LLM tool-calling decisions align with human preferences under varying amounts of elapsed time. Our analysis reveals that existing models display poor alignment with human temporal perception, with no models achieving a normalized alignment rate better than 65{\%} when given time stamp information. We also show that naive, prompt-based alignment techniques have limited effectiveness for most models, but specific post-training alignment can be a viable way to align multi-turn LLM tool use with human temporal perception. Our data and findings provide a first step toward understanding and mitigating temporal blindness, offering insights to foster the development of more time-aware and human-aligned agents."
}