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 Cached Hidden States for Efficient Rollback (Archer), a training-free caching method for rollback-capable DLMs. Archer recomputes the mutable response at every update while reusing prompt K/V within a bounded response-state neighborhood. This asymmetric boundary amortizes prompt computation without restricting rollback. Bounded reuse also delays feedback from tentative tokens while keeping response ...
| Authors | Xuning He, Qingyuan Shi, Zinan Sheng et al. |
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| Primary category | cs.CL |
| Published | 8 August 2026 |
| Last updated at source | 15 October 2026 |
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