@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models
%A Li, Chenhao
%A Song, Dandan
%A Zhou, Changzhi
%A Yang, Jun
%A Tian, Yuhang
%A Ma, Huipeng
%A Feng, Guangyuan
%A Zhang, Luan
%A Li, Xudong
%A Duan, Ke
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-static
%X 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.
%U https://aclanthology.org/2026.findings-acl.92/
%P 1913-1932
Markdown (Informal)
[Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models](https://aclanthology.org/2026.findings-acl.92/) (Li et al., Findings 2026)
ACL
- Chenhao Li, Dandan Song, Changzhi Zhou, Jun Yang, Yuhang Tian, Huipeng Ma, Guangyuan Feng, Luan Zhang, Xudong Li, and Ke Duan. 2026. Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 1913–1932, San Diego, California, United States. Association for Computational Linguistics.