Prefilling-dLLM: Predictive Prefilling for Long-Context Inference in Diffusion Language Models
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Computer Science > Computation and Language
arXiv:2606.10537 (cs)
[Submitted on 9 Jun 2026]
Title:Prefilling-dLLM: Predictive Prefilling for Long-Context Inference in Diffusion Language Models
Authors:Jing Xiong, Qi Han, Shansan Gong, Yunta Hsieh, Chengyue Wu, Chaofan Tao, Chenyang Zhao, Ngai Wong
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Abstract:Diffusion large language models (dLLMs) re-encode the entire prefix at every denoising step, causing recomputation that scales
quadratically with context length and becomes prohibitive for long-context scenarios. We propose Prefilling-dLLM, a training-free
prefill-decode disaggregation framework for dLLMs that partitions the prefix into N chunks, caches their KV representations once,
and selects the top-K most relevant chunks with intra-chunk token sparsity for decoding, showing that sparse prefilling can
outperform dense attention while reducing per-step complexity from quadratic in the full sequence length to quadratic only in the
decode length. On LongBench and InfiniteBench, Prefilling-dLLM achieves state-of-the-art quality among dLLM acceleration methods,
and an attention kernel that parallelizes decoding over the non-contiguously cached chunk KV yields 9.1--28.0x speedup at 8K--32K
contexts. We further show that beginning-of-sequence tokens prepended to each chunk act as periodic attention anchors that eliminate
the lost-in-the-middle phenomenon. Code is available at this https URL.
| Comments: | Technical Report |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.10537 [cs.CL] |
| (or arXiv:2606.10537v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.10537
arXiv-issued DOI via DataCite (pending registration)
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