The Translucent Universe: An Epistemological Shift toward Mechanistic Forensics and Dynamic Stabilization in Large World Models This paper formalizes Synthetic Neuroscience (SN) as a critical paradigm for navigating the transition from linguistic emulation to the ontological internalization of physical laws within Large World Models (LWMs). Central to this framework is the AngelFall Paradox, a mathematical proof demonstrating that high-dimensional probabilistic intelligence is thermodynamically incompatible with static safety constraints, rendering traditional alignment methods obsolete in the face of superintelligent emergence. To address this fundamental instability, the research introduces Mechanistic Latent Forensics (MLF)—a diagnostic methodology utilizing the Jacobian of Causality to extract "Latent Physics" from high-dimensional manifolds—and Pyragas Delayed Feedback Control (TDFC), a non-invasive stabilization technique derived from non-linear dynamics. By treating AI as a translucent proxy for physical reality, this framework offers a unified theory for the forensic analysis and real-time control of chaotic systems, ranging from tokamak plasma stability to climate tipping points, thereby transforming the "Black Box" of artificial intelligence into a transparent instrument for civilizational discovery.