Temporal Renormalization: Solving the Horizon Explosion via Multiscale Diffusion Planning

Why can AI write code but fail to plan a complex project? The bottleneck isn't data; it's the physics of time. Current models suffer from a "Horizon Explosion," where errors accumulate exponentially over time. This makes long-horizon reasoning mathematically impossible for standard Transformers. This paper proposes a solution: Temporal Renormalization — a blueprint for AGI planning that treats time as a fractal. By treating time as a fractal hierarchy instead of a linear sequence, we reduce planning complexity from linear to logarithmic, allowing AI to maintain stability over thousands of steps. We introduce the Fractal Diffusion architecture (SWE-DP), a "CEO-Manager-Worker" stack that uses multiscale physics to solve the reasoning logjam. Instead of predicting the next token, this system minimizes energy across a trajectory of constraints. We invite the community to fork this primitive and test it against the "10k Step Challenge." The full architectural blueprint, mathematical proofs, and engineering stack are available in the whitepaper. Break the logjam. MIT License c. 2026 Annen20