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
arXiv AI
Sep 10

Do New Attention Mechanisms Actually Fix Attention Sinks at Million-Token Context?

The paper investigates whether recent attention‑mechanism improvements—specifically gated attention, Kimi K3, Kimi Delta Attention, and Attention Residuals—effectively eliminate the attention‑sink problem when scaling language models to a one‑million‑token context window. Using a new diagnostic suite called SinkProbe, the authors evaluate sink mass, massive activation, position‑resolved recall, and the recency gap across four small models that vary only in token mixing and depth. Their findings show that the training objective, rather than the architecture, drives the emergence of attention sinks; gating did not replicate its previously reported benefits at the larger scale, and sink mass, activations, and positional bias behaved independently.

By Sara Rizwan, Samaanah Abdus Salam
arXiv AI
Sep 21

RBS-Attention: Radius-Bounded Sparse Prefill for Long-Context Large Language Models

RBS-Attention introduces a training‑free, radius‑bounded sparse prefill strategy for long‑context large language models, addressing the mean dilution problem where a block centroid can miss highly relevant tokens. The method employs two complementary selection branches: a centroid base branch that captures average relevance and a rescue branch that uses the maximum key‑block radius to flag under‑estimated blocks. Experiments on Qwen3 models demonstrate significant speedups—over 20× in standalone prefill‑attention and nearly 6× in end‑to‑first‑token time—while maintaining competitive accuracy compared to dense attention.

By Chuxu Song, Jiuqi Wei, Zhencan Peng