Title: Characterizing the Nature of Inference in Active Inference

Original: arXiv:2606.04935v1 Announce Type: new Abstract: Active inference casts decision-making as inference, with the Expected Free Energy (EFE) unifying goal-directed and information-seeking behavior. Recent work showed that EFE minimization can be written as Variational Free Energy (VFE) minimization on a generative model augmented with epistemic priors. We prove that the VFE of the augmented model can be rewritten as the VFE of the predictive model plus explicit entropy-correction terms, making the EFE contribution transparent. We then show that proper EFE-based planning requires combining these epistemic corrections with a planning correction that turns marginal inference into policy optimization, yielding a full variational characterization of EFE-based planning. This clarifies which corrections are needed for cross-entropy planning and for full EFE-based planning. The same entropy-corrected formulation leads to a detailed message-passing scheme for EFE-based planning together with simpler ablations. Experiments on three grid-world environments show that the planning correction already helps when observations are decisive, whereas the additional observation-side epistemic corrections matter most when observations are merely suggestive.

Rewritten: Title: Understanding the Inference Mechanisms Underlying Active Inference

Original: arXiv:2606.04935v1 Announce Type: new Abstract: Active inference reframes decision-making through the lens of inference, utilizing Expected Free Energy (EFE) to harmonize behaviors aimed at goals with those driven by curiosity. Previous studies demonstrated that minimizing EFE is equivalent to minimizing Variational Free Energy (VFE) within a generative model enhanced by epistemic priors. In this work, we demonstrate that the VFE associated with this augmented model can be decomposed into the VFE of the standard predictive model along with distinct entropy-correction components, thereby clarifying the specific role of EFE. Furthermore, we establish that effective planning based on EFE necessitates integrating these epistemic adjustments with a planning-specific modification that shifts the process from marginal inference to policy optimization. This integration provides a comprehensive variational description of EFE-driven planning. This analysis delineates the specific corrections required for both cross-entropy planning and complete EFE-based planning. The entropy-corrected framework also supports a sophisticated message-passing algorithm for EFE planning, alongside more streamlined variants. Empirical tests across three grid-world scenarios reveal that while the planning correction offers benefits when observations are conclusive, the supplementary epistemic corrections tied to observations are crucial when those observations are only indicative.

Source: arXiv Generated at: 2026-06-04 00:00:00 UTC