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arXiv · 2606.08565

EinSort: Sorting is All We Need for Tensorizing LLM

Toshiaki Koike-Akino·Jing Liu·Ye Wang
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Abstract

Tensor networks provide efficient representations for compressing large neural networks. By carefully designing shapes and topologies, they can significantly reduce memory and computational costs. However, identifying implicit low-rank structures in large foundation models remains challenging due to their enormous scale and un-structured weight distributions. We propose an adaptive tensorization method that discovers inherent low-rank structure in a target tensor by index ordering. Experiments on weight and KV-cache compression demonstrate improved reconstruction quality compared to baselines.

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Toshiaki Koike-Akino, Jing Liu, Ye Wang. 2026-06-07. EinSort: Sorting is All We Need for Tensorizing LLM. https://arxiv.org/abs/2606.08565

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