Chen Fang

EMAN: Optimization-Driven Capacity Growth through Path Emergence in Multi-Task Learning

The Emergent Modular Atomic Network (EMAN), an optimization-driven framework for exposing an antisymmetric growth direction through latent relative phases without instantiating a second path, and for monitoring multiple decision signals during training to transform local optimization evidence into a structural decision is proposed.