Free Energy Principle

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1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Belief-Based Maximum Occupancy Principle and Active Inference
Sep 30, 2026Manolis Mylonas, Rubén Moreno BoteBelief-Space Planning Intrinsic Reward Methods
Center for Brain and Cognition & Department of Engineering Universitat Pompeu Fabra Barcelona, Spain
Expected Free Energy as Belief-Dependent Utility for rho-POMDPs
Jul 18, 2026Patrick Cooper, Alvaro VelasquezFree Energy Calculation Active Inference
Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA
Principled Direction-Free Intrinsic Motivation through Model-Free Epistemic Free-Energy Estimators
Jul 18, 2026Alireza Furutanpey, Schahram DustdarFree Energy Calculation Intrinsic Reward Methods
Coovally, Barcelona, Spain · Distributed Systems Group, TU Vienna, Austria · ICREA Barcelona, Spain
The SIGReg Objective as Variational Free Energy: A Theoretical Active-Inference Account of JEPA World Models
Jul 15, 2026Fabio Arnez, Alexandra Gomez-VillaWorld Model Learning Active Inference
Université Paris-Saclay, CEA, List F-91120, Palaiseau, France · Computer Vision Center Barcelona, Spain
Constructed Reality, Contested Priors: Decoupling and the Architecture of Cognitive Relapse Under the Free Energy Principle
Jul 12, 2026MD Ibrahim Hossain RidoyComputational Cognitive Modeling Free Energy Principle
Independent Researcher
Free Energy Heuristics: Fast-And-Frugal Cognition as Active Inference Under Uncertain Precision
Jun 14, 2026Alex BogdanCognitive Modeling Active Inference
Evolutionairy AI Toronto, Canada
Expected Free Energy-based Planning as Variational Inference
Jun 9, 2026Wouter W. L. Nuijten, Thijs van de Laar, Bert de VriesFree Energy Calculation Active Inference
Eindhoven University of Technology, Eindhoven, the Netherlands · Lazy Dynamics B.V., the Netherlands
What Type of Inference is Active Inference?
Jun 3, 2026Wouter W. L. Nuijten, Mykola Lukashchuk, Thijs van de Laar +1Active Inference Free Energy Principle
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands · Lazy Dynamics, Utrecht, the Netherlands
Agent-Centric Social Trajectory Prediction: A Free Energy Principle Perspective
May 25, 2026Yanping Wu, Ji Zhang, Hao Chen +2Multi-Agent Trajectory Prediction Probabilistic Forecasting
University of Glasgow Glasgow, UK · Southwest Jiaotong University Chengdu, China
Cultivating Machine Intelligence: The OMEGA Shift from Top-Down Optimization to Autopoietic Cognitive Ecologies
May 24, 2026Ata G. ZareCognitive Modeling Evolutionary Optimization
Nyenrode Business University
A Game Theoretic Free Energy Analysis of Higher Order Synergy in Attention Heads of Large Language Models
May 10, 2026Djamel BouchaffraLLM Pruning Attention Head Analysis
DAVID Lab, University of Paris-Saclay, UVSQ Campus, 78035 Versailles, France
On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective
May 8, 2026Yuhao Li, Shengchao LiuSupervised Fine-Tuning Free Energy Principle
Department of Computer Science and Engineering The Chinese University of Hong Kong
Information as Maximum-Caliber Deviation: A bridge between Integrated Information Theory and the Free Energy Principle
May 3, 2026Alexander KearneyFree Energy Principle
University of Oxford
Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts
May 1, 2026Man Yung WongSparse Mixture-of-Experts Mixture-of-Experts Inference
Russell · Independent Researcher
A Collective Variational Principle Unifying Bayesian Inference, Game Theory, and Thermodynamics
Apr 30, 2026Djamel Bouchaffra, Faycal Ykhlef, Mustapha Lebbah +1Nash Equilibrium Cooperative Game Theory
1DAVID Lab, University of Paris-Saclay, UVSQ, 78035 Versailles, France. · 2TSAM, ASM, Centre for Development of Advanced Technologies, Algiers, Algeria. · 3LIPN, UMR CNRS 7030, Sorbonne Paris Nord University, Villetaneuse, France.
Active Inference for Physical AI Agents -- An Engineering Perspective
Mar 21, 2026Bert de VriesActive Inference Embodied AI
Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands
Efficient and robust control with spikes that constrain free energy
Mar 10, 2026André Urbano, Pablo Lanillos, Sander KeeminkRobust Control Spiking Neural Networks
Cajal Neuroscience Center, Spanish National Research Council, Madrid, Spain · Department of Machine Learning and Neural Computing, Donders Institute for Brain Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
Learning-Based Robust Control: Unifying Exploration and Distributional Robustness for Reliable Robotics via Free Energy
Mar 6, 2026Hozefa Jesawada, Giovanni Russo, Abdalla Swikir +1Robust Control Distributionally Robust RL
Mechanical Engineering Program, New York University Abu Dhabi, Abu Dhabi, UAE. · Department of Information and Electrical Engineering & Applied Mathematics, University of Salerno, Fisciano, Italy. · Mohammed bin Zayed University for Artificial Intelligence, Abu Dhabi, UAE.
Seeing Through Uncertainty: Free-Energy-Inspired Real-Time Adaptation for Robust Visual Navigation
Mar 4, 2024Maytus Piriyajitakonkij, Rishabh Dev Yadav, Mingfei Sun +2Visual Navigation Test-Time Adaptation
Department of Computer Science, The University of Manchester, United Kingdom · College of Computing and Data Science, Nanyang Technological University, Singapore · Institute of Advanced Intelligence and Computing (IAIC), Agency for Science, Technology and Research (A*STAR), Singapore

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Belief-Based Maximum Occupancy Principle and Active Inference

Manolis Mylonas, Rubén Moreno Bote

Center for Brain and Cognition & Department of Engineering Universitat Pompeu Fabra Barcelona, Spain

Belief-Space Planning Intrinsic Reward Methods Active Inference Partially Observable RL Free Energy Principle

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Intrinsic motivation plays a central role in adaptive and goal-directed behavior by conferring agents reward-independent objectives and biases useful to act in noisy and uncertain environments. Active Inference addresses the problem of acting in a partially observable environment through a principled framework for belief updating and action selection. A key component of Active Inference is the specification of prior preferences, which shapes behavior by encoding desirable future outcomes. An intrinsic motivation approach called the Maximum Occupancy Principle (MOP) proposes that agents act so as to maximize occupancy over future paths of states and actions, with no preferences or epistemic targets. Despite its simple formulation, MOP gives rise to rich and adaptive behaviors that combine exploratory variability with goal-directed dynamics. In this work, we extend MOP to partially observable environments and introduce a Bellman reformulation of the Expected Free Energy for Active Inference, both incorporating belief-based inference over hidden states as part of the agent state. The Bellman formulation enables tractable offline computation via value iteration over the full belief-state space. We compare the resulting behaviors in a set of minimal experimental settings with uncertain food sources. We find that MOP agents switch between goal-directed (food seeking) behavior and exploration between different food sources, depending on their energy available and their belief state. In contrast, Active Inference agents mostly inhabit regions around a single food source, a strategy having both high pragmatic and epistemic value. We finally compare with Empowerment, which is shown to be qualitatively similar to Active Inference.