Shubham Barmase

Deterministic Spatial Planning on Hypercomplex Manifolds: 1-Pass Maze Navigation with Zero Backpropagation and 53 KB Memory Footprint

Standard Reinforcement Learning (RL) and Frontier Foundation Models face severe structural bottlenecks in spatial planning. Deep RL algorithms (e.g., PPO, DreamerV3) suffer from extreme sample complexity (10^6–10^8 interactions) due to gradient dilution across delayed reward horizons, while Large Language Models (LLMs)...