Kernel-based Interpolatory Methods for Context Compression of Attention Mechanisms
arXiv:2610.40943v1 Announce Type: new Abstract: The bottleneck of attention mechanisms, which are the fundamental building block of modern language models, is their quadratic complexity in terms of context size. There has thus been an increasing interest in the compression of the context, also called KV-cache compression. In this article, we consider the problem of compressing the context of attention mechanisms by first formulating it in the Hilbert space associated with its kernel. In this formalism, attention can be represented as the ratio of two linear maps whose interpolant is also the r
阅读原文 · arXiv 机器学习本文由 SoccerDream AI 自动聚合索引，内容版权归原作者与 arXiv 机器学习 所有。
本站仅作技术动态聚合，不构成任何形式的建议。
本站仅作技术动态聚合，不构成任何形式的建议。