Recursive M¨obius Encoding for Dynamic Context Topology

Standard Large Language Models (LLMs) rely on linear attention mechanisms that suffer from entropic decay as sequence length increases. We propose a novel architecture based on **In- formation Substrate Theory (IST)** that replaces the static context window with a dynamic **M¨obius Recursive Manifold**. Rather than processing text as a linear block, the system en- codes information into a self-referential topological loop. When the information density exceeds the manifold’s capacity, the system triggers a **Golden Ratio (ϕ) Recursive Shift**, creating a fractal sub-layer of encoding. This allows for constructive information retention rather than decay, bounded only by the hardware’s floating-point bit-depth (e.g., FP64 parity limits). We discuss how this mimics ”Active Inference,” granting the algorithm a form of causal agency over its own memory structure.