The Coherence Trap Why Classical Computing Assumptions Become Failure Modes in Recursive Intelligence Systems

Abstract Classical computing was built upon a collection of assumptions that enabled reliable operation within bounded and externally constrained environments. Among these assumptions were: • determinism, • separation of code and data, • closed-world reasoning, • consistency as evidence of correctness, • and failure through termination. These assumptions proved successful because traditional computational systems execute instructions rather than construct world models. Recursive intelligence systems operate differently. They generate explanations, integrate abstractions, infer missing structure, and recursively modify the conceptual frameworks through which subsequent reasoning occurs. This paper argues that several foundational assumptions of classical computing become unstable when applied to recursive intelligence systems. The central failure emerges when coherence acquires survivorship independent of constraint. Coherence is defined as internal explanatory consistency. Constraint is defined as external falsifiability, empirical resistance, adversarial rejection, bounded termination, and host-domain override authority. Both are necessary. Neither is sufficient alone. The paper introduces the concept of the coherence trap: a condition in which recursive systems progressively substitute coherence for constraint and increasingly mistake explanatory closure for reality contact. The central claim is therefore narrow: consistency is not equivalent to correctness. In recursive systems, coherence remains admissible only while subordinate to constraint. The governing invariant is: [ \boxed{ \text{coherence without constraint converges toward epistemic drift} } ] ⸻ 1. Introduction Classical software systems execute procedures. Recursive intelligence systems construct interpretations. This distinction creates a fundamental architectural shift. A compiler does not generate ontologies. A database does not recursively revise its understanding of reality. A sorting algorithm does not develop explanatory frameworks about the objects it processes. Modern intelligence systems increasingly perform all three. As recursive reasoning increases, systems become less defined by execution and more defined by interpretation. The critical question therefore changes. The question is no longer: “Did the system execute correctly?” The question becomes: “How does the system remain constrained by reality while recursively generating explanations about reality?” Classical computing largely assumes that consistency indicates correctness. Recursive intelligence systems expose the limits of that assumption. A system may remain internally coherent while becoming progressively detached from external constraint. The resulting failure is not computational. It is epistemic.