arXiv AI
More Value per Key: Asymmetric Sparse Attention for Faster LLM Decoding
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Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage
arXiv:2601. 03043v4 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing significant demands on inference efficiency.
arXiv AI
Jun 9
Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps
arXiv:2605. 16928v2 Announce Type: replace-cross Abstract: Long-context inference in large language models is bottlenecked by the quadratic cost of full attention.
arXiv Computation and Language
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Scaling Parameter and Context in Attention: Native Sparse Attention from Mixture-of-Head
The paper introduces NAMOH, a native sparse attention mechanism that activates only a subset of heads per token, allowing each head to attend to a limited subsequence of tokens. By scaling the number of heads while keeping the active heads per token fixed, the method shortens head histories and reduces key‑value access without increasing overall storage. Experiments demonstrate that NAMOH can outperform fully activated models with the same parameter count and enable more efficient long‑context inference than smaller dense models.
arXiv AI
Jul 21
SelKV: Selective KV Cache Merging with Per-Token Merge-or-Drop and Attention Compensation
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arXiv Machine Learning
Jul 28
DynaCalKV: Key-Value Cache Compression via Head Grouping and Adaptive Rank Allocation
arXiv:2607. 24331v1 Announce Type: new Abstract: As the inference phase of Large Language Models (LLMs) requires handling long context windows, the Key-Value (KV) cache initially appears to address this challenge but eventually becomes a significant bottleneck as the context window continues to grow.
arXiv Machine Learning
Sep 23
CompKV: Compensation-Aware KV Selection for Long-Context LLM Inference
CompKV introduces a compensation‑aware sparse attention framework for long‑context LLM inference. It partitions tokens into blocks and optimizes token selection to minimize the error introduced by block‑level mean compensation, using compact block‑level statistics. Experiments on RULER and LongBench‑Pro show CompKV outperforms other sparse baselines and achieves up to a 6.85× speedup over full attention.