@inproceedings{li-etal-2026-static,
    title = "Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models",
    author = "Li, Chenhao  and
      Song, Dandan  and
      Zhou, Changzhi  and
      Yang, Jun  and
      Tian, Yuhang  and
      Ma, Huipeng  and
      Feng, Guangyuan  and
      Zhang, Luan  and
      Li, Xudong  and
      Duan, Ke",
    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.92/",
    pages = "1913--1932",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models are trained on static corpora but deployed in a dynamic world, leading to systematic temporal failures{---}from mis-anchored expressions and inconsistent timelines to hallucinated future events, stale world knowledge, and related issues. Existing surveys on temporal knowledge graphs, retrieval-augmented generation, hallucination, and knowledge editing cover only isolated fragments of this space: they are typically task-centric and do not offer a holistic theoretical account of how frozen LLMs represent and reason about time. This survey provides a unified perspective on temporal reasoning in LLMs. We formalize temporal queries in an information-theoretic framework based on the parametric reachability of temporal premises and answers, which induces four temporal information regimes corresponding to internal reasoning, answer recency, premise anchoring, and genuine world indeterminacy. Under this lens, we delineate the landscape of temporal failure modes, consolidate methodologies for diagnosing temporal deficiencies, and synthesize mitigation approaches into a coherent design space. Together, these contributions provide a systematic roadmap toward reliable time-aware large language models."
}