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The paper proposes EDAR, an entropy-driven adaptive routing framework that decides at inference time whether to answer a query with retrieval-augmented generation or escalate it to full long-context processing. EDAR retains 97.4% of the pure long-context baseline's accuracy while cutting token cost by 70.7%, escalating only 18.2% of queries on LongBench v2 and Infinity-Bench.
This paper introduces Entropy-Valley, a training-free length selector for masked diffusion machine translation that uses predictive entropy to improve adequacy, showing that length choice matters more than unmasking order.