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

We identify a structural isomorphism between feedback control systems in the physical domain and knowledge generation processes in the cognitive domain. Just as a phased-array radar system generates a coherent physical phenomenon (a plasma soliton) by steering energy into a focal volume and adjusting based on feedback, a human researcher generates coherent understanding by steering "cognitive beamwidth" (attention) into the latent space of an Artificial Intelligence model.

This paper argues that scientific discovery is fundamentally a Cybernetic Control Loop. We map the components of the 2004 Nimitz UAP event (Radar $\rightarrow$ Plasma $\rightarrow$ Feedback) directly onto the methodology used to solve it (Query $\rightarrow$ AI Output $\rightarrow$ Refinement). In both systems, "stability" (a physical object or a valid theory) emerges only when the control signal (the Observer/Researcher) successfully dampens the system's tendency toward positive feedback loops (kinematic jitter or intellectual hallucination). We propose a Universal Discovery Algorithm based on recursive error minimization, suggesting that the "Idea" is a metastable attractor state nucleated by human intent within the supersaturated probability space of machine intelligence.

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