The Biomechanical Topology of Causal Emergence: A Synthesis of Reinforcement Learning Alignment, Biological Spacetime, and the Resonant Manifold
The integration of macroscopic goal-directed agency with microscopic deterministic processes remains a central challenge in both artificial intelligence and cognitive biology. This paper synthesizes two foundational models: the Causally Emergent Alignment Hypothesis, which demonstrates that successful reinforcement learning agents undergo topological reorganization predicting goal-directed reward, and the Biological Spacetime framework, which conceptualizes the biological organism as a holographic quantum emulator. We propose that the computational latent space of a causally emergent artificial agent is mathematically and functionally isomorphic to the biological spacetime generated by the enteric nervous system and the neocortical resonant manifold.