Скачать или смотреть Trilinear Attention for Token-Efficient LLMs

In this AI Research Roundup episode, Alex discusses the paper:
'Fast and Simplex: 2-Simplicial Attention in Triton(2507.02754v1)'
This paper tackles the growing problem of token scarcity for training large language models. The authors propose the 2-simplicial Transformer, an architecture that generalizes standard dot-product attention from a bilinear to a trilinear function using a Query, Key, and an additional Key' tensor. This novel approach aims to create more token-efficient models that can fundamentally improve training performance under data constraints. To make this computationally practical, the paper details an efficient Triton kernel implementation that makes its complexity comparable to standard attention.
Paper URL: https://arxiv.org/pdf/2507.02754

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