Cub³: A New Heuristic Architecture for Cross-Domain Convergence

This paper introduces Cub³, a heuristic architecture for symbolic convergence across three distinct epistemic domains: computation, mathematics, and physics. It is not a method of verification, but of convergence. Cub³ defines a symbolic space in which conceptual entities are generated, evaluated, and recombined according to their structural survivability under multidomain tension. Rather than seeking binary validation within any one discipline, Cub³ employs a model of partial adherence. Each symbolic construct is evaluated across multiple axes of domain alignment, producing heterogeneous but interpretable signals of epistemic viability. These scores do not imply truth, but functional resonance: a construct may achieve 91% adherence to computational heuristics, 68% in physical plausibility, and 42% in mathematical formalism — without disqualifying its inclusion. The architecture functions entirely within simulated symbolic environments. No empirical instrumentation is used. The model operates by internal recomposition, abstraction drift, and symbolic collapse — seeking not proof, but persistence. This work does not assert knowledge; it designs a structure where knowledge candidates can be tested for coherence under diverse symbolic constraints.