High school student seeking advice: Found an architectural breakthrough that scales a 17.6B model down to 417M?

A high school student from Japan, operating under the pseudonym 'Monolith,' has posted a potentially groundbreaking claim in an online AI forum. The student, who develops AI architectures as a hobby, states they have created a custom neuron-based search algorithm designed to find 'optimal equations.' Using this method, they report discovering a novel technique that drastically compresses large language models. The specific achievement is reducing a model with the architecture of a standard 17.6B parameter LLM (4096 dimensions, 64 layers, SwiGLU activation) down to a functionally comparable model with only 417 million parameters—a 42x reduction in size.

This compressed 4096-dimension, 64-layer model is reportedly running on the student's personal laptop, a feat normally impossible for models of that scale. To validate the core mathematical concepts, the student shared the design specifications and equations (but not the source code) with Anthropic's Claude AI, which confirmed the mathematical reproducibility. An initial search suggests these specific equations have no prior published instances in AI literature. The student is now seeking advice from the professional community on the next steps for formal verification, peer review, and publication, as they navigate the academic process for the first time.