GENESIS: A Neuromorphic Generative Architecture for Continuous Multimodal Learning

Abstract:

We present Genesis, a novel neural architecture that transcends the limitations of transformer-based models through biomimetic design principles and hybrid computational paradigms. Genesis integrates state space models, mixture-of-experts routing, neuromorphic computing elements, and structured reasoning modules to achieve superior performance across multiple dimensions. The architecture addresses fundamental transformer limitations including quadratic complexity, fixed context windows, catastrophic forgetting, and limited reasoning capabilities. Through theoretical analysis and architectural innovations, we demonstrate how Genesis achieves linear computational complexity, unbounded context processing, continuous learning without forgetting, and multi-level reasoning capabilities spanning analog computation, chain-of-thought, and tree-of-thought paradigms. We provide complete mathematical formulations with detailed derivations, training procedures, implementation details, and deployment strategies while identifying open research questions for future investigation.

The PDF file can be downloaded using the following link:

https://www.dropbox.com/scl/fi/8in0m7k38iugh8trb533m/GenesisPaper205.pdf?rlkey=752cgzl4mfxe743wjmuur8x93&st=kbk847ud&dl=0