LLMs Without Deep Neural Networks: New Architecture, Benefits and Case Study
This paper introduces a novel LLM architecture based on RBF networks that eliminates deep neural networks, finds the global optimum of the loss function in closed form in one iteration, and offers improved explainability and accuracy.
[2605.30385] LLMs Without Deep Neural Networks: New Architecture, Benefits and Case Study
[Submitted on 28 May 2026]
Title:LLMs Without Deep Neural Networks: New Architecture, Benefits and Case Study
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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.
Comments: 9 pages, 5 figures
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.30385 [cs.LG]
(or arXiv:2605.30385v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.30385
arXiv-issued DOI via DataCite
Submission history
From: Vincent Granville [view email] [v1] Thu, 28 May 2026 07:34:15 UTC (285 KB)
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