arXiv:2602.03216v3 Announce Type: replace-cross Abstract: The quadratic complexity of attention remains the central bottleneck in long-context inference for large language models. Prior acceleration methods either sparsify the attention map with structured patterns or permanently evict tokens at specific layers, which can retain irrelevant tokens or rely on irreversible early decisions despite the layer-/head-wise dynamics of token importance. In this paper, we propose Token Sparse Attention, a lightweight and dynamic token-level sparsification mechanism that compresses per-head $Q$, $K$, $V$
The quadratic complexity of attention in large language models requires urgent solutions for efficient long-context inference as demand for AI capabilities grows.
This development addresses a fundamental bottleneck in LLMs, directly impacting the scalability and cost-efficiency of advanced AI applications, crucial for industries leveraging long-context processing.
The ability to process much longer contexts with improved efficiency becomes more feasible, potentially unlocking new applications for LLMs that were previously computationally prohibitive.
- · AI model developers
- · Cloud computing providers
- · Enterprises leveraging generative AI
- · Edge AI hardware manufacturers
- · Inefficient AI inference architectures
- · Companies reliant on older, less optimized LLM deployments
Reduced computational costs and increased context windows for large language models.
Accelerated development and deployment of more sophisticated AI agents and applications requiring extended memory.
A potential shift in AI application design, favoring solutions that intensely leverage long-context understanding for more complex tasks and fewer human-in-the-loop interventions.
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Read at arXiv cs.LG