H2Q-MicroStream is a highly experimental deep learning architecture built on Occam’s razor and holographic principles, aiming to explore the physical dynamics of language models. Unlike mainstream Transformers, this project uses a quaternion spatiotemporal attention mechanism to upgrade attention from scalar products to four-dimensional spatiotemporal interference, forcing the model to extract core patterns rather than rote memorization through Rank-8 essential constraints. It innovatively processes byte streams directly using Unicode, eliminating the need for BPE Tokenizers, and simulates biological neuron learning patterns through micro-batch high-frequency updates. The architecture emphasizes internalized thinking over linguistic expression and prioritizes state maintenance over historical recall, representing a completely new approach to neural network design. The project is open-source, providing complete installation and operation guides along with configuration parameters, offering AI researchers a new tool to explore the essence of LLMs.
