Time-Decay-Aware Reasoning: Maintaining Large Language Model Knowledge Freshness Without Full Retraining

Large language models store factual knowledge in their parameters, but this knowledge is fixed at the training cut-off point and becomes outdated as the world changes; therefore, although fluent, the answers may be incorrect. A time-decay-aware reasoning framework has been introduced in this paper to maintain such knowledge over time without retraining or modifying the model’s parameters. The framework assigns a decay rate to each fact based on how often it is updated in a temporal knowledge base, converts this rate into the probability that a parametric answer has expired, and only issues a retrieval call when this probability exceeds a threshold; thus, retrieval is focused on facts that have actually aged. On TempLAMA (50,310 temporally scoped Wikidata questions, 2010–2020), the method outperforms the fine-tuning and editing baselines, recovers about two-thirds of the accuracy gap between a frozen model and exhaustive retrieval while issuing only half as many retrieval calls; it does not modify static knowledge. The Design aims to run large language models efficiently in the face of continuous fact updates.