提出无需深度神经网络的LLM新架构，一步求解全局最优。
LLMs Without Deep Neural Networks: New Architecture, Benefits and Case Study
- 用RBF网络机制替代传统DNN，实现闭式解析求解
- 单次迭代即达损失函数全局最优，省去训练过程
- 适合追求可解释性与高效推理的LLM研究者
本文旨在验证一种替代深度神经网络（DNN）的LLM新架构。近期中国研究者对径向基函数（RBF）网络表现出浓厚兴趣，因其具备更高可解释性和准确率。本文提出的模型虽独立发现，但基于完全相同的机制，且有关键突破：不依赖DNN，可在单次迭代中以闭式解求得损失函数全局最优，彻底消除传统训练步骤。文中提供技术概览、案例研究及与类似方法的对比分析。
原文摘要 · Abstract (English)
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.
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