← Back to all news
arXiv Computation and Language By Jiwon Song, Yoongon Kim, Jae-Joon Kim

Joint Subspace Sparse Attention for Efficient Long-Context Decoding

Read the original on arXiv Computation and Language →

Joint Subspace Sparse Attention (JSSA) is a new method that reduces KV‑cache traffic in long‑context LLM decoding by selecting tokens at the token level while preserving query–key structure. It learns a shared low‑dimensional query–key subspace through a tractable spectral solution and implements a gather‑free indexed‑attention kernel that supports per‑KV‑head selections. Across various attention architectures, JSSA maintains near full‑KV accuracy, avoids generation‑length inflation, and can achieve up to 6.43× decoding throughput on a single GPU for 128K context, even at 700B‑parameter scale.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv AI
Jun 4

Stochastic Sparse Attention for Memory-Bound Inference

arXiv:2605. 01910v2 Announce Type: replace-cross Abstract: Autoregressive decoding becomes bandwidth-limited at long contexts, as generating each token requires reading all $n_k$ key and value vectors from KV cache.

By Kyle Lee, Corentin Delacour, Kevin Callahan-Coray, Kyle Jiang, Can Yaras, Samet Oymak, Tathagata Srimani, Kerem Y. Camsari
llmsefficiency
More like this →
arXiv Machine Learning
Sep 21

Elastic Threshold Attention: Learned Contextual Sparsity for Long-Context Decoding

Elastic Threshold Attention (ETA) is a trainable attention mechanism that dynamically predicts contextual thresholds from query representations, enabling selective pruning of KV cache tokens during long‑context decoding. By multiplicatively suppressing sub‑threshold logits during training, ETA avoids representation collapse and eliminates localized attention sinks, allowing a 1.45B model to match dense attention performance at roughly 85% training sparsity and 38% active decode density. At inference, a custom Triton kernel achieves up to 2.5× faster decoding on sequences up to 512K tokens, and an offline calibration step can further reduce compute by 27% by freezing per‑head thresholds.

By Themistoklis Haris, Henry Li, Maryam Karimzadehgan
efficiency
More like this →
arXiv AI
Jun 12

MiniMax Sparse Attention

arXiv:2606. 13392v1 Announce Type: new Abstract: Ultra-long-context capability is becoming indispensable for frontier LLMs: agentic workflows, repository-scale code reasoning, and persistent memory all require the model to jointly attend over hundreds of thousands to millions of tokens, yet the quadratic cost of softmax attention makes this untenable at deployment scale.

By Xunhao Lai, Weiqi Xu, Yufeng Yang, Qiaorui Chen, Yang Xu, Lunbin Zeng, Xiaolong Li, Haohai Sun, Haichao Zhu, Vito Zhang, Pengyu Zhao
llmsagentsefficiencymultimodal
More like this →