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.

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.

Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)

arXiv:2607.20708 [cs.LG]

(or arXiv:2607.20708v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.20708 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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

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