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

arXiv: https://arxiv.org/abs/2605.30385 · HTML: https://mlsanity.com/paper/2605.30385/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 28 May 2026.

## Builds on (most cited first, 1 of 1)

- [Nonlinearity as Rank: Generative Low-Rank Adapter with Radial Basis Functions](https://mlsanity.com/paper/2602.05709/) (2026)

## Built on by (most cited first, 1 of 1)

- [Piercing Gilbreath's Conjecture: From Deep Number Theory Insights to Fintech and Cybersecurity](https://mlsanity.com/paper/2607.04166/) (2026)

## Abstract (arXiv)

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.