AlphaCOGANT: Recursive Corporate Self-Improvement as Active Inference

Catalog Row178

Citation KeyFriedman2026AlphaCOGANTRecursiveCorporateSelf178

Paper FolderAvailable

DOI10.5281/zenodo.20976824

Platform availability

✅ Zenodo
✅ GitHub
⬜ arXiv
⬜ OSF
⬜ HuggingFace
⬜ Software Heritage
⬜ PyPI
✅ Full documentation

Overview

Extracted from the local paper documentation when available.

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...

active inferenceexpected free energyrecursive self-improvementGeneralized Notation Notationeconomic world modelportfolio optimizationepistemic valuereproducible research

Use Notes

Concise findings and methods pulled from README/SKILL documentation.

Findings / Concepts

t-RSI metric recovers as standardized EFE-improvement certificate for recursive self-improvement
Corporate EWM maps to five-channel hidden-state factors: Investments, Sensors, Actuators, Parameters, R&D
Epistemic value equals information gain about EWM purchased by Sensors/R&D (data-scaling law)
Filtration integrity constraint enforces no-peeking discipline in corporate modeling

Methods / Techniques

Generalized Notation Notation (GNN) modeling
Active Inference simulation
Portfolio optimization analysis
State inference

Citation

Plain-text citation for quick reuse.

Friedman, Daniel Ari. 2026. AlphaCOGANT: Recursive Corporate Self-Improvement as Active Inference. Zenodo. DOI: 10.5281/zenodo.20976824. URL: https://doi.org/10.5281/zenodo.20976824.

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