Paper Link: https://arxiv.org/abs/2604.04921

Summary:

The provided sources introduce TriAttention, a novel KV cache compression technique designed to enhance the efficiency of Large Language Models during long-context reasoning. By identifying that query and key vectors concentrate around stable centers in the pre-RoPE space, the researchers developed a trigonometric series to predict and retain the most important tokens. This method overcomes the instability of traditional post-RoPE observation windows, which often suffer from memory bottlenecks and information loss. Experimental results demonstrate that TriAttention matches the accuracy of Full Attention while reducing memory usage by 10.7x and increasing throughput by 2.5x. Ultimately, this framework enables the deployment of complex reasoning models on limited hardware, such as a single consumer GPU, without sacrificing performance on mathematical or general tasks.