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AsyncTLS: Efficient Generative LLM Inference with Asynchronous Two-level Sparse Attention

Yuxuan Hu, Jianchao Tan, Jiaqi Zhang, Wen Zan, Pingwei Sun, Yifan Lu, Yerui Sun, Yuchen Xie, Xunliang Cai, Jing Zhang · Apr 9, 2026 · Citations: 0

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Apr 9, 2026, 5:15 AM

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Apr 9, 2026, 5:15 AM

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Abstract

Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory. While token-level sparse attention offers superior accuracy, its indexing overhead is costly; block-level methods improve efficiency but sacrifice precision. We propose AsyncTLS, a hierarchical sparse attention system that combines coarse-grained block filtering with fine-grained token selection to balance accuracy and efficiency, coupled with an asynchronous offloading engine that overlaps KV cache transfers with computation via temporal locality exploitation. Evaluated on Qwen3 and GLM-4.7-Flash across GQA, and MLA architectures, AsyncTLS achieves accuracy comparable to full attention while delivering 1.2x - 10.0x operator speedups and 1.3x - 4.7x end-to-end throughput improvements on 48k - 96k contexts.

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Evidence snippet: Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory.

Evaluation Modes

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Automatic metrics

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Evidence snippet: Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory.

Quality Controls

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Evidence snippet: Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory.

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Evidence snippet: Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory.

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provisional

Accuracy

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Evidence snippet: While token-level sparse attention offers superior accuracy, its indexing overhead is costly; block-level methods improve efficiency but sacrifice precision.

Rater Population

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Evidence snippet: Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory.

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  • Potential evaluation modes: Automatic metrics
  • Potential metric signals: Accuracy
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Research Brief

Deterministic synthesis

Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory.

Generated Apr 9, 2026, 5:15 AM · Grounded in abstract + metadata only

Key Takeaways

  • Long-context inference in LLMs faces the dual challenges of quadratic attention complexity and prohibitive KV cache memory.
  • While token-level sparse attention offers superior accuracy, its indexing overhead is costly; block-level methods improve efficiency but sacrifice precision.
  • We propose AsyncTLS, a hierarchical sparse attention system that combines coarse-grained block filtering with fine-grained token selection to balance accuracy and efficiency, coupled with an asynchronous offloading engine that overlaps KV cache transfers with computation via temporal locality exploitation.

Researcher Actions

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