Beyond the Ergodic Wall: A Discrete Geometric Physics Sandbox for Analysing AI Scaling Limits and Complexity Collapse
Read the original on arXiv AI →The paper identifies a fundamental limitation in current deep learning, describing an "ergodic ceiling" that forces models to converge to statistical averages of existing human knowledge. It proposes a discrete geometric physics sandbox—built on a Holographic E8 Projection Engine and a lattice of Planck‑scale spheres—to inject non‑ergodic, path‑dependent insight and enforce thermodynamic constraints. By modeling spacetime as nested FCC lattices and using topological constraints, the sandbox aims to prevent model collapse, recover quantum mechanics via renormalisation‑group flows, and potentially collapse NP‑to‑P complexity by restricting algorithmic search to physically conserved trajectories.
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