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LLMs Without Deep Neural Networks: New Architecture, Benefits and Case Study

arxiv.org
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Abstract:The purpose of this article is to provide validation to my deep neural network alternative in the context of LLMs. Very recently, there has been a significant interest by Chinese researchers in a model called RBF network, as a substitute to standard DNNs, with increased explainability and higher accuracy. It turns out that my new model, discovered independently, is based on the exact same machinery. But with a major twist: it does not need DNN as it finds the global optimum of the loss function in closed form, in one iteration, thus eliminating the tedious training step. Here I provide a high-level overview of my technology, with case study and comparison to similar methods.

Submission history

From: Vincent Granville [view email]
[v1] Thu, 28 May 2026 07:34:15 UTC (285 KB)