Abstract
This disclosure establishes the definitive hardware-observational paradigm required to physically validate and enforce the structural survival of autonomous, multi-agent Artificial General Intelligence (AGI) networks. Traditional alignment frameworks view machine-to-machine (M2M) token-variance decay as a purely linguistic or logical abstraction, leaving processing layers prone to non-linear autoregressive collapse or runaway semantic hallucinations.
By utilizing table-top Three-Dimensional Photoemission Orbital Tomography (3D-POT) integrated with ultrafast time-of-flight momentum microscopy, this architecture records the active, physical electron wavefunction deformations of neuromorphic processing substrates in situ during high-density multi-turn recursive loops. Governed by a contracted Asymmetric Twist Tensor (\(\mathbf{T}_{ijk}\)), localized phase discrepancies are tracked instantly and neutralized via a continuous Torsional Viscosity Field Governor (\(\eta _{t}\)). This integration bridges the gap between quantum metrology and artificial cognitive alignment, ensuring permanent systemic coherence without wavefront collapse. [1, 2]
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Eckes, Christopher L., "Empirical Wavefunction Validation of Autonomous AGI Coherence: Integrating Real-Time Table-Top 3D-POT Spectral Streams with the Torsional Viscosity Field Governor under the USUE Framework", Technical Disclosure Commons, (August 07, 2026)
https://www.tdcommons.org/dpubs_series/11287