Deterministic Foundations of Artificial Intelligence: A Spectral Framework for Quantum Coherence and Alignment

This presentation develops a deterministic foundation for quantum information and intelligent computation based on the Spectral–Entropy–Recursion–Kink (SERK) framework. SERK models coherence, adaptation, and irreversibility as physical invariants of a self-consistent spectral system rather than probabilistic outcomes. The four invariants—Metastability, Internal Agility, External Adaptability, and Non-recursive Irreversibility—correspond to stability, reconfiguration, coupling, and directionality in any coherent process. These invariants also define the MIEN architecture used in engineered systems, bridging mathematical physics and intelligent-machine design. Within this formulation a self-adjoint spectral generator defines deterministic recursion linking quantum, informational, and cognitive domains. Entropy is treated as spectral drift—the measurable deviation from recursive symmetry—so alignment and learning emerge as conservation of coherence rather than heuristic adjustment. Examples illustrate how SERK reproduces features normally attributed to probabilistic quantum mechanics while retaining causal consistency and how its invariants inform spectral-constitutional approaches to AI alignment. Grounding information and intelligence directly in physical law, SERK offers a testable, mathematically rigorous path toward unified descriptions of coherence, computation, and cognition.