In a reward-free active inference agent, Φr localizes to the slow perspective latent g, its aggregate magnitude comes from the recurrent architecture rather than learning, and learning only becomes visible as a decoupling sign flip plus regime-invariant stability.
Proceedings of the National Academy of Sciences122(39), e2423297122 (2025)
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Perspective Latents as an Architectural Condition for Causal Emergence in Active Inference Agents
In a reward-free active inference agent, Φr localizes to the slow perspective latent g, its aggregate magnitude comes from the recurrent architecture rather than learning, and learning only becomes visible as a decoupling sign flip plus regime-invariant stability.