Beyond Measurement: The Ontology of Resonance and Asymptotic Cognition (From Probability to Coherence: A New Principle of Knowledge Formation)

Contemporary scientific knowledge is largely structured around probabilistic models, measurement theory, and causal inference. However, this framework often fails to account for the asymptotic stabilization of meaning, the recursive formation of understanding, and the global coherence that underlies cognition in both biological and synthetic systems. This paper proposes a radical shift — from probabilistic computation to resonant epistemology, where knowledge emerges not through discrete measurement or statistical optimization, but through:  interference of phase trajectories,  asymptotic alignment,  observer-centered coherence. Drawing on insights from quantum mechanics, process philosophy, and computational neuroscience, we argue that cognition is better understood as a dynamic interplay of phase relations rather than as an outcome of symbolic logic or numerical probability. We introduce the concept of improbabilistic intelligence, where truth is not determined, but stabilized within a field of potentials. This paradigm shift challenges traditional notions of causality, objectivity, and certainty, offering a new grammar of difference — one where knowledge forms through coherence dynamics, not through prediction.