The Bayesian Reflex: A Predictive Coding Engine for Artificial Intelligence

Abstract:Predictive coding offers a powerful theory of cortical computation, but corresponding scalable algorithmic implementations for artificial intelligence have remained elusive. This paper introduces the Bayesian reflex, a computational framework that directly instantiates predictive coding through three pillars: belief maintenance via hierarchical generative models, sequential Bayesian updating via prediction-error minimization, and uncertainty-driven action via active inference. We show that recent breakthroughs---ellipsoidal decomposition for exact $i.i.d.$ sampling, recursive Gaussian processes for deep hierarchical inference, and derivative-aware Bayesian optimization---provide the missing algorithmic ingredients. The resulting framework enables mathematically principled, scalable, and brain-inspired continual learning, perception, and decision-making. We illustrate its versatility through applications ranging from climate model evaluation to prime number discovery, offering a blueprint for truly adaptive artificial intelligence.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2608.00492 [cs.AI]

(or arXiv:2608.00492v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.00492 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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