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.

Automatic fact verification (FV) predicts the veracity of a claim against retrieved evidence, yet modern verifiers often behave as shortcut-driven verifiers, predicting from surface regularities in the claim rather than the factual content of the evidence. Existing debiasing methods over-correct this by uniformly enforcing evidence reliance on every sample, thereby suppressing the valid information already carried by the claim itself. We propose Evidence-Grounded Remixing for Fact Verification (EGR-FV), which estimates a per-sample evidence necessity score by contrasting claim-with-evidence and claim-only predictions, and uses it to remix training batches so that debiasing is adapted to each sample’s actual reliance on evidence. On FEVER, PolitiHop, and HOVER together with their symmetric counterparts, EGR-FV achieves state-of-the-art accuracy and robustness, validating the effectiveness of EGR-FV.