DRAFT - Recursive Synthesis: A Control Theory of Epistemology in Human-AI Systems

We identify a structural invariant—the observer–system coupling operator C—that governs feedback dynamics whenever observation perturbs the observed. This triple-update loop (observation generates artifact; artifact updates system; artifact updates observer) recurs across quantum measurement, radar–plasma interaction, perceptual learning, financial markets, and scientific theory change. The structure is general; outcomes depend on regime. This paper focuses on one convergent regime: Recursive Synthesis, the process by which knowledge advances through iterated human–AI interaction under sufficient constraint. We demonstrate a structural isomorphism between feedback control systems in the physical domain and knowledge generation in the cognitive domain. Just as a phased-array radar system generates a coherent physical phenomenon by steering energy into a focal volume and adjusting based on feedback, a human researcher generates coherent understanding by steering attention into the latent space of an AI model. Scientific discovery, we argue, is fundamentally a cybernetic control loop. We map the components of the 2004 Nimitz UAP event (radar -> plasma -> feedback) directly onto the methodology used to solve it (query -> AI output -> refinement). In both systems, stability—a physical object or a valid theory—emerges only when the observer successfully dampens the system's tendency toward positive feedback (kinematic jitter or intellectual hallucination). Recursive Synthesis is the epistemic instantiation of C in human–AI collaboration: a generalizable discovery process based on recursive error minimization, in which candidate ideas function as metastable attractor states nucleated by human intent within the supersaturated probability space of machine intelligence. We formalize the coupling operator, define convergence criteria via information-theoretic measures, enumerate failure modes (runaway self-confirmation, hallucination drift, paradigm lock-in), and identify the structural requirements—distributed observation, external constraint, interface design—that determine whether recursion converges toward discovery or collapses into artifact. This work forms part of a broader research program examining how rendering, constraint, and convergence emerge in uncertain physical, cognitive, and social systems. Research Program Invitation - https://grodriguez6.github.io/amo-collab