The SIGReg Objective as Variational Free Energy: A Theoretical Active-Inference Account of JEPA World Models

Jul 16, 2026

A new preprint demonstrates that the choice of anti-collapse regularizer in Joint-Embedding Predictive Architectures (JEPAs) determines whether their training objective aligns with Active Inference (AIF) variational free energy. The authors organize four regularizers into an entropy-estimator hierarchy and prove that SIGReg uniquely eliminates the prior-miscalibration gap, making the objective an exact information bottleneck. They also identify a key AIF term—state-epistemic value—not computed by current JEPA world models.

Why it matters: This work provides a normative theoretical foundation for JEPA world models by linking their objectives to active inference, potentially guiding future model design and evaluation.

Full story at: arXiv Machine Learning ↗