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Research questionHow can shared-prefix KV caching accelerate diffusion language model serving without corrupting context-dependent KVs?In diffusion language models, bidirectional attention allows token updates to alter the context represented by previously computed keys and values. Reusing a prefix cache as though those representations were fixed can therefore cause severe accuracy loss.
AI
Diffusion Models
Inference Optimization
Machine Learning
Natural Language Processing
Technology
Latest papersRecent research connected to this question, newest first.Enabling KV Caching of Shared Prefix for Diffusion Language ModelsThis concerns high-throughput serving of diffusion language models with shared-prefix requests. The source provides experimental evidence for dynamically reusing prefix KVs at selected shallow layers, reporting throughput and accuracy comparisons with existing caching techniques.research paper · Sep 2, 2026
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