The article introduces a new approach to contrastive decoding in large language models, shifting from a context-aware to a conflict-aware paradigm that better manages discrepancies between external context and model priors. This method, along with the proposed evaluation protocol TriState-Bench and Adaptive Regime Routing, aims to enhance the reliability and accuracy of language model outputs by dynamically balancing context and prior knowledge.