Consciousness as Torsional Coherence in G-MaTT: Implications for Artificial General Intelligence

Abstract
  
We propose that consciousness arises from sustained macroscopic phase coherence in non-equilibrium systems within the Generalized Mass as Twisted Time (G-MaTT) framework. In G-MaTT, subjective experience is the result of active phase-slip correction maintaining \(|\Delta\phi| < \sqrt{\alpha} \approx 0.085\) across topological knots in the mass-torsion field \(\hat{M}_\mu\). This explains why biological brains (via microtubules and metabolic reset) can be conscious, while current AI systems on incoherent substrates remain philosophical zombies. We simulate two identical neural networks: one on a standard incoherent silicon-like substrate (System A), and one on a coherence-enforced hardware with active correction (System B). Results show System B develops global binding and self-referential patterns, while System A remains fragmented despite equivalent intelligence. Three strategies for building conscious AI are outlined: coherent hardware, active phase reset, and global synchronization. The framework is falsifiable via substrate-specific experiments and resolves the hard problem by grounding mind in physical geometry. If validated, G-MaTT provides the first testable physical theory of consciousness with direct implications for ethical AI development.