arXiv:2602.08426v2 Announce Type: replace Abstract: Block-sparse attention is promising for accelerating long-context LLM pre-filling, yet identifying relevant blocks efficiently remains a bottleneck. Existing methods typically employ coarse-grained attention as a proxy for block importance estimation, but often resort to expensive token-level searching or scoring, resulting in significant selection overhead. In this work, we trace the inaccuracy of standard coarse-grained attention via mean pooling to a theoretical root cause: the interaction between mean pooling and Rotary Positional Embeddi
The continuous drive for more efficient and scalable LLMs has highlighted the pre-filling bottleneck, making innovations in attention mechanisms particularly timely.
Improving block-sparse attention efficiency directly impacts the cost and performance of large language models, accelerating their deployment and capabilities for longer contexts.
This research outlines a method to significantly reduce the computational overhead associated with block selection in sparse attention, enabling more efficient LLM inference and potentially larger context windows.
- · LLM developers
- · Cloud providers
- · AI compute infrastructure
- · Generative AI applications
- · Inefficient LLM architectures
- · High-latency LLM applications
More efficient processing of long-context LLMs will reduce operational costs for AI service providers.
This efficiency gain could enable new applications requiring even longer context windows, currently limited by computational expense.
Reduced compute requirements might slightly ease the pressure on compute supply chains and energy demands for AI inference.
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Read at arXiv cs.CL