How can active inference be scaled to complex models with many degrees of freedom while maintaining tractable inference?

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• Bayesian model expansion allows for generalisation and concept learning in active inference.claim0.791
  Definition of Bayesian model expansion, Section 9.2.
• The key challenge for active inference is finding the generative model that best explains observable data.claim0.786
  Identifies an outstanding problem, Section 10.
• In brief, active inference proposes that agents achieve this by optimising two complementary objective functions, a variational free energy and an expected free energy.quote0.784
  Concise statement of the core hypothesis from Section 2.
• Active inference offers an attractive natural adaptation mechanism for non-stationary environments due to its Bayesian model updating properties.claim0.781
  §3, after non-stationary results.
• Active inference LLMs extending prediction-focused language models with tighter perception-action feedback loops may naturally embody contemplative wisdom as they scalehypothesis0.779
  Predictive hypothesis about Contemplative Architecture approach based on Petersen et al. 2025 work
• Active inference offers a degree of plausibility as a process theory of brain function.claim0.778
  Overall assessment from Discussion.
• Active inference agents can learn epistemic habits in the absence of extrinsic rewards through ambiguity minimization.claim0.770
  §2, summarizing information-seeking behavior.
• Active inference agents can carry out epistemic exploration and account for uncertainty about their environment in a Bayes-optimal fashion.claim0.768
  Abstract and §1, summarizing a key property.