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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What to verify

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Evidence quality

Provisional

Derived from abstract and metadata only.

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.

Abstract-only analysis — low confidence

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Should You Rely On This Paper?

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Best use

Background context only

Use if you need

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Main weakness

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Trust level

Provisional

Usefulness score

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Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

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Provisional (processing)

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What We Could Verify

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Human Feedback Types

provisional (inferred)

None explicit

No explicit feedback protocol extracted.

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

Evaluation Modes

provisional (inferred)

Automatic metrics

Includes extracted eval setup.

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

Quality Controls

provisional (inferred)

Not reported

No explicit QC controls found.

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

Benchmarks / Datasets

provisional (inferred)

Not extracted

No benchmark anchors detected.

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

Reported Metrics

provisional (inferred)

Accuracy

Useful for evaluation criteria comparison.

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

Rater Population

provisional (inferred)

Unknown

Rater source not explicitly reported.

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

Human Feedback Details

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Potential human-data signal: No explicit human-data keywords detected.
Potential benchmark anchors: No benchmark names detected in abstract.
Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Details

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Potential evaluation modes: Automatic metrics
Potential metric signals: Accuracy
Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

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

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

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.

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Signals below are heuristic and may miss details reported outside the abstract.

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