14 papers
Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification
Karim Zaghw, Andrew Pashea, Marc Pritsch +3
Active inference offers a unified framework for perception, learning, and action, but scaling discrete active-inference models to rich spatial and temporal domains remains difficul…
Meta-Representational Predictive Coding: Neuroscience-Informed Self-Supervised Learning
Alexander Ororbia, Karl Friston, Rajesh P. N. Rao
Self-supervised learning has become an increasingly important paradigm in the domain of machine intelligence. Furthermore, evidence for self-supervised adaptation, such as contrast…
The adaptive nature of confirmation bias
Dorje C. Brody, Karl J. Friston, Bernhard K. Meister +1
In this paper, the phenomenon generally classified as confirmation bias is formulated on the space of square-root probabilities (or equivalently, using the structures of quantum pr…
Active Inference as the Test-Time Scaling Law for Physical AI Agents
Omar Hashash, Christo Kurisummoottil Thomas, Walid Saad +3
In this paper, a novel test-time scaling law for physical artificial intelligence (AI) agents is introduced. This scaling law enables physical AI agents to reason with their world…
A theory of generalised coordinates for stochastic differential equations
Lancelot Da Costa, Nathaël Da Costa, Conor Heins +5
Stochastic differential equations are ubiquitous modelling tools in physics and the sciences. In most modelling scenarios, random fluctuations driving dynamics or motion have some…
A Rosetta Stone Hypothesis for Neurophenomenology: Mathematical Predictions from Predictive Processing
Lancelot Da Costa, Anil K. Seth, Karl Friston +2
Consciousness science faces the challenge of bridging first-person experience with third-person empirical measurements. Neurophenomenology aims to build such `generative passages'…