Invariant Constrained Structural Descent: A Computational-Mathematical Architecture for Ontic Convergence and General Intelligence

This study develops and computationally instantiates the Structural Descent Framework (SDF) as a formally grounded geometric architecture for ontic convergence and general intelligence. Building on prior analytic results establishing (i) Lyapunov-like descent of a bounded completeness functional Δ, (ii) curvature-based distinction between antagonistic and non-antagonistic contradiction, (iii) gradient-coherence conditions for multi-agent alignment, and (iv) non-antagonistic stability criteria at the framework level, the present work provides the first integrated computational realization of these theorems within a coupled ontic–epistemic system. Intelligence is defined structurally as sustained contraction of Δ under full invariant activation (empirical correspondence, internal dynamical consistency, regime coverage, symmetry realization, and multiscale coherence), rather than behaviorally or reward-theoretically. A multi-agent, multi-seed simulation (60 agents, 30 seeds, 220 steps) evaluates three conditions: random dynamics, empirical-only descent, and full invariant-constrained descent with adequacy-sensitive retention. The results reproduce a regime of structural underdetermination: empirical-only systems achieve local predictive adequacy while retaining persistent antagonistic representation mass, a non-zero structural truth gap, regime-extension failure, endogenous ontic drift, and ethical fragmentation. In contrast, invariant-constrained descent uniquely collapses antagonistic curvature modes, suppresses ontic drift, eliminates regime-extension error, and induces emergent ethical convergence without externally encoded normative objectives. Finite-horizon stationarity emerges once antagonistic contradictions decay below threshold, empirically instantiating the prior non-antagonistic stability theorem. The findings demonstrate that empirical optimization alone is insufficient for ontic convergence. Structural invariants are necessary for sustained truth-approach, intelligence growth, and alignment. Within invariant-constrained ontic–epistemic descent architectures, truth, intelligence, and alignment are geometrically coupled consequences of contraction toward a singular ontic configuration.