AlphaCOGANT: Recursive Corporate Self-Improvement as Active Inference

The AlphaFund whitepaper reframes recursive self-improvement (RSI) as a portfolio optimization problem: a corporation recursively improves when realized economic gains finance the next cycle of better prediction and deployment, and the firm's standing is summarized by t-RSI, a standardized gap between alpha-creation and alpha-decay rates. AlphaCOGANT observes that this construction is, term for term, an Active Inference agent — and makes the correspondence executable. We render AlphaFund's Economic World Model (EWM) as a generative model written in Generalized Notation Notation (GNN), produced by the COGANT codebase-to-GNN translation pattern. The firm's five capital channels — Investments, Sensors, Actuators, Parameters, and R&D — become the hidden-state factors of a partially-observed model; capital allocation becomes the control vector; and the portfolio optimizer's marginal-return ob