Towards Generalization of Block Attention via Automatic Segmentation and Block Distillation
The paper discusses advancements in block attention mechanisms for processing long-context scenarios. It introduces a new dataset for semantic segmentation and a training framework called block distillation. These innovations aim to enhance the efficiency and effectiveness of block attention in various applications.
- ▪Block attention processes input as separate blocks, improving KV cache reuse in long-context scenarios.
- ▪The authors constructed a large semantic segmentation dataset with over 30k instances across 16 categories.
- ▪Block distillation is proposed as a more efficient training framework that achieves near-full-attention performance.
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Computer Science > Computation and Language arXiv:2605.15913 (cs) [Submitted on 15 May 2026] Title:Towards Generalization of Block Attention via Automatic Segmentation and Block Distillation Authors:Shuaiyi Li, Zhisong Zhang, Yan Wang, Lei Zhu, Dongyang Ma, Chenlong Deng, Yang Deng, Wai Lam View a PDF of the paper titled Towards Generalization of Block Attention via Automatic Segmentation and Block Distillation, by Shuaiyi Li and 7 other authors View PDF HTML (experimental) Abstract:Block attention, which processes the input as separate blocks that cannot attend to one another, offers significant potential to improve KV cache reuse in long-context scenarios such as Retrieval-Augmented Generation (RAG).
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