31 citations · 72 across the 17 of their papers we have counts for
19 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…
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…
Active inference and artificial reasoning
Karl Friston, Lancelot Da Costa, Alexander Tschantz +4
This technical note considers the sampling of outcomes that provide the greatest amount of information about the structure of underlying world models. This generalisation furnishes…
Sensory robustness through top-down feedback and neural stochasticity in recurrent vision models
Antonino Greco, Marco D'Alessandro, Karl J. Friston +2
Biological systems leverage top-down feedback for visual processing, yet most artificial vision models succeed in image classification using purely feedforward or recurrent archite…
AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models
Conor Heins, Toon Van de Maele, Alexander Tschantz +11
Current deep reinforcement learning (DRL) approaches achieve state-of-the-art performance in various domains, but struggle with data efficiency compared to human learning, which le…