🚀 Revolutionary MoE Architecture Research

The Revolutionary Journey

Traditional Mixture of Experts (MoE) models route tokens to a small subset of specialized experts, but what if we could enable all experts to collaborate? And what if we could fundamentally change how we train models?

This project chronicles a five-phase research evolution that culminates in Geometric Constrained Learning (GCL) - the world's first training paradigm that optimizes data presentation rather than model weights.

Traditional Training: Adjust model weights to fit data
Geometric Constrained Learning: Adjust data presentation to fit fixed model geometry

This paradigm shift has achieved remarkable results: 46% improvement in total loss, 96% improvement in expert specialization, and runs on consumer hardware like MacBook.

Each phase below represents a significant architectural breakthrough, building toward the revolutionary GCL system that fundamentally changes how we think about machine learning training.

Architectural Evolution

Phase 1: GNN-MoE

Dense expert activation with Graph Neural Network coupling. The foundation of collaborative expert architectures.

Phase 2: HGNN-MoE

Evolution to Hypergraph Neural Networks, enabling higher-order expert interactions and multi-expert coalitions.

Phase 3: Adaptive Orthogonality

Intelligent specialization through adaptive orthogonal constraints. 99.7% expert specialization achieved.

Phase 4: Ghost Expert HGNN-MoE

Adaptive capacity scaling with dormant experts activated on-demand. Intelligent resource allocation for varying task complexity.

🚀 Phase 5: Geometric Constrained Learning

REVOLUTIONARY BREAKTHROUGH: World's first training paradigm that optimizes data presentation rather than model weights. 46% loss improvement!

📖 User Manual

Complete guide to using the MoE Research Hub. Interactive CLI, configuration wizard, training workflows, and dataset management.

Revolutionary Breakthrough Achievements