Скачать или смотреть 1-Bit Liquid Metal Neural Network (LMNN) Author: Anthony Pyper

excerpts from a thesis and a subsequent academic paper by Anthony Pyper, describes the 1-Bit Liquid Metal Neural Network (LMNN), an innovative computational architecture designed for extreme memory efficiency on constrained devices. The LMNN achieves this efficiency through binary quantization (using only 1-bit weights and activations) and integrates biologically inspired features such as self-repairing neurons that utilize cached states and thermal modulation. Beyond typical neural processing, the framework introduces a Hybrid Symbiotic State System that evolves symbolic states (the Fundamental Triad: MONAD, DUALITY, TRIAD) influenced by quantum-like dynamics and nervous-system analogies, aiming to balance robust dynamics with ultra-low resource usage. The demonstration shows that this novel system can achieve resilient adaptation and maintain stable internal harmony despite perturbations.