Perspective Latents as an Architectural Condition for Causal Emergence in Active Inference Agents

arXiv:2607.20708v1 Announce Type: new Abstract: A recent line of work measures causal emergence in reinforcement learning agents through Integrated Information Decomposition, reporting that Phi_r grows with training and tracks reward improvement. For active inference, this raises the question of how reward-free predictive organization relates to such information-theoretic signatures. I test this within an active inference agent whose architecture separates a fast perception latent z from a slow global latent g, where g is driven by prediction error and structurally decoupled from policy gradients. In a reward-free environmental regime-switching protocol, Phi_r concentrates in g; its aggregate magnitude is largely architectural and decreases with training. The substantive effect of learning becomes legible only at the atom-compositional level: decoupling flips sign from negative to positive and becomes regime-invariant under environmental change, while downward causation carries the regime-dependent adjustment. These results identify g as the architectural locus of Phi_r-relevant temporal organization in an active inference agent, and argue against reading scalar Phi_r as a direct index of learned integration.

Source: arXiv cs.LG | 2026-07-24