When Facts Change: Temporal Knowledge Conflict Resolution in LLMs

Retrieval-augmented generation (RAG) systems require large language models (LLMs) to reconcile discrepancies between their parametric memory-knowledge encoded during training-and contextual inputs provided at inference. When these sources conflict, models often exhibit unstable reasoning and inconsistent factual behavior. We investigate how LLMs resolve such conflicts when the discrepancy arises from temporal misalignment-facts that have changed since the model's knowledge cutoff-and whether mutability, the changeability of facts, can serve as a mediating signal in this process. To do so, we provide WIKIRE-CENTCHANGES, a temporally grounded benchmark with stable and recently updated facts derived from Wikidata. Our results show that while models spontaneously produce temporal reasoning for facts that actually changed -but almost never for stable ones -this differentiation rarely propagates to their final predictions. Explicitly prompting them to consider mutability increases references to temporal change but does not improve factual accuracy, revealing a disconnect between verbalized reasoning and prediction behavior. We further show that the failure point is scale-dependent: smaller models rarely detect the underlying conflict, while larger models detect it but fail to act on their mutability judgments.