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This paper investigates the architectural locus of causal emergence in active inference agents, finding that a slow global latent variable (g) serves as the primary site for integrated information (Φr) and that scalar Φr should not be interpreted as a direct measure of learned integration.
Shows that minimizing Expected Free Energy (EFE) is equivalent to solving a ρ-POMDP with utility as expected information gain, with exploration weight fixed at 1. Proves equivalence for observe-then-commit POMDPs and extends to factored observation POMDPs, with experiments demonstrating untuned weight matches or outperforms reward-only planning.
This paper establishes a formal correspondence between the SIGReg objective used in Joint-Embedding Predictive Architectures (JEPAs) and the Active Inference framework, showing that under certain conditions the training objective becomes an exact variational free energy. It organizes non-contrastive regularisers into an entropy-estimator hierarchy and proves theoretical results including exact information bottleneck and preserved surprise bounds for SIGReg.
A solo-built multi-agent cognitive architecture uses hyperbolic geometry on a Poincaré ball manifold, variational free energy for belief updating, and wave interference for memory retrieval, allowing personality to emerge from memory interactions rather than scripting.
The paper proposes an active inference controller for adaptive traffic signal control in noisy IoT environments, outperforming DQN in idle times and CO2 emissions under sensor occlusion and adverse weather conditions.
Waymo and Delft University of Technology published a paper in Nature Communications describing ReD, a cognitive model that simulates how human drivers make split-second decisions to avoid collisions, which can serve as a benchmark for autonomous driving systems.
This paper models cancer treatment as a belief-space planning problem using active inference, deriving an expected free-energy objective that unifies goal-directed control and information acquisition under measurement budgets. The framework is validated on real clinical data from the AACR Project GENIE, demonstrating simultaneous patient categorization and high treatment efficacy.
This paper analyzes Active Inference by proving that the Variational Free Energy of an augmented generative model can be decomposed into the predictive model's VFE plus explicit entropy-correction terms, yielding a full variational characterization of Expected Free Energy-based planning. The authors derive a message-passing scheme for EFE-based planning and validate it on grid-world environments.
This paper proposes a minimal architecture for body-grounded perspective formation in artificial agents, extending prior work with an interoceptive viability signal and conative alignment mechanism to operationalize machine subjectivity from a phenomenological perspective.
Seed IQ achieves a perfect 14/14 score on ARC-AGI-3 games using an active inference, physics-driven multi-agent autonomous control engine, as shown in a behind-the-scenes video walkthrough.