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arXiv · 2605.12193

BFLA: Block-Filtered Long-Context Attention Mechanism

Chong Wu·Zhenan Feng·Renjie Xu·Houwang Zhang·Jiawang Cao·Maolin Che·Wenbo Zhu·Hong Yan
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Abstract

This paper proposes Block-Filtered Long-Context Attention (BFLA), a training-free sparse prefill attention mechanism for long-context inference. BFLA adopts a two-stage design. In Stage 1, query and key sequences are compressed into coarse blocks, and lightweight block-level softmax mass estimation is performed to construct an input-dependent block importance mask. In Stage 2, the coarse mask is expanded to the Triton attention-tile grid. Several tile-level rescue strategies are applied to reduce information loss, where a fused sparse prefill kernel skips unimportant KV tiles while preserving exact token-level attention inside every retained tile. BFLA requires no retraining, calibration, preprocessing, or model modification and can be plugged into existing vLLM-style paged-attention workloads. Experiments on Gemma 4, Llama 3.1, Qwen 3.5, and Qwen 3.6 series models show that BFLA substantially accelerates long-context prefilling with minimal accuracy degradation compared to dense Triton FlashAttention. Project website: https://github.com/Alicewithrabbit/BFLA.

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Chong Wu, Zhenan Feng, Renjie Xu, Houwang Zhang, Jiawang Cao, Maolin Che, Wenbo Zhu, Hong Yan. 2026-05-12. BFLA: Block-Filtered Long-Context Attention Mechanism. https://arxiv.org/abs/2605.12193

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