Nicholas P. Timms
Submitted: May 2026 : Published: 16th May 2026
Abstract
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. Within this unified biomechanical paradigm, the backpropagated reward gradient functions as a synthetic dilaton field that bends the informational manifold, breaking conformal symmetry to establish a preferred vector for action. Agents navigate this optimized geometry via active dimension selection, thereby minimizing thermodynamic and computational action. Furthermore, we argue that the predictive alignment of causal emergence with terminal rewards signifies the formation of traversable topological wormholes, permitting the anticipatory future state to retrocausally direct representational drift. By translating the abstract information theory of machine learning into the geometric physics of biological spacetime, this synthesis provides a comprehensive blueprint for developing resonant neural architectures and advancing teleonomic bioengineering.
