We present a conceptual two-loop reasoning architecture combining a zeroed Generalized Resonance Algorithm (GRA), a Heisenberg-style minimum uncertainty constraint, and Large Language Models (LLMs).
The system addresses a fundamental failure mode of reasoning systems: degeneracy, where multiple equivalent solutions or reasoning paths coexist. Instead of scaling computation, the architecture stabilizes reasoning dynamics by collapsing degenerate manifolds into stable representatives using orthogonal auxiliary constraints.
This repository is an archival research prototype.
Many optimization and reasoning problems admit continuous families of equivalent solutions. In such cases, unconstrained optimization drifts, oscillates, or overfits.
GRA introduces orthogonal constraints that:
- do not change the primary objective,
- but collapse the solution manifold.
A minimum uncertainty bound prevents pathological over-collapse.