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Archer: Adaptive Reuse of Cached Hidden States for Efficient Rollback in Diffusion Language Models
Diffusion language models (DLMs) can revise earlier predictions as context evolves, but rollback requires costly full-sequence recomputation. Conventional key-value (KV) caching assumes immutable historical states and is difficult to reconcile with such revision. We introduce Adaptive Reuse of Cache
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