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    <abstract>Diffusion large language models (dLLMs) present a promising alternative to dominant autoregressive models (ARMs) by the ability of parallel decoding at the expense of substantial computation and memory costs. Specifically, the cache mechanism for bidirectional attention in dLLMs demands large memory footprint, restricting their ability to handle long contexts under resource-limited settings. Existing cache eviction strategies are primarily designed for ARMs and fail to account for the role of mask tokens and specific characteristics in dLLMs, resulting in suboptimal performance.To address these challenges, we introduce MaskKV, a training-free cache eviction framework tailored to dLLMs, focusing on the effect of mask tokens in dLLMs. MaskKV is built on two key innovations: (1) a mask-query guided scoring mechanism that leverages attention weights to identify and evict less critical prompt tokens for each head; (2) an adaptive cache budgeting strategy that improves efficiency by reducing allocation in intermediate layers and concentrating resources on prompt-preferring heads. On LLaDA with MaskKV, compressing the KV cache to only 256 pairs (less than 5% of tokens) retains 94% of the full-cache performance on LongBench and achieves up to 31 \times acceleration at 32k prompt length. Our code will be released on Github.</abstract>
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%0 Conference Proceedings
%T Mask Tokens as Prophet: Fine-Grained Cache Eviction for Efficient dLLM Inference
%A Huang, Jianuo
%A Zhang, Yaojie
%A Yang, Yicun
%A Huang, Benhao
%A Zhang, Linfeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F huang-etal-2026-mask
%X Diffusion large language models (dLLMs) present a promising alternative to dominant autoregressive models (ARMs) by the ability of parallel decoding at the expense of substantial computation and memory costs. Specifically, the cache mechanism for bidirectional attention in dLLMs demands large memory footprint, restricting their ability to handle long contexts under resource-limited settings. Existing cache eviction strategies are primarily designed for ARMs and fail to account for the role of mask tokens and specific characteristics in dLLMs, resulting in suboptimal performance.To address these challenges, we introduce MaskKV, a training-free cache eviction framework tailored to dLLMs, focusing on the effect of mask tokens in dLLMs. MaskKV is built on two key innovations: (1) a mask-query guided scoring mechanism that leverages attention weights to identify and evict less critical prompt tokens for each head; (2) an adaptive cache budgeting strategy that improves efficiency by reducing allocation in intermediate layers and concentrating resources on prompt-preferring heads. On LLaDA with MaskKV, compressing the KV cache to only 256 pairs (less than 5% of tokens) retains 94% of the full-cache performance on LongBench and achieves up to 31 \times acceleration at 32k prompt length. Our code will be released on Github.
%U https://aclanthology.org/2026.findings-acl.170/
%P 3456-3479

Markdown (Informal)

[Mask Tokens as Prophet: Fine-Grained Cache Eviction for Efficient dLLM Inference](https://aclanthology.org/2026.findings-acl.170/) (Huang et al., Findings 2026)

• Mask Tokens as Prophet: Fine-Grained Cache Eviction for Efficient dLLM Inference (Huang et al., Findings 2026)

ACL

• Jianuo Huang, Yaojie Zhang, Yicun Yang, Benhao Huang, and Linfeng Zhang. 2026. Mask Tokens as Prophet: Fine-Grained Cache Eviction for Efficient dLLM Inference. In Findings of the Association for Computational Linguistics: ACL 2026, pages 3456–3479, San Diego, California, United States. Association for Computational Linguistics.