Active inference LLMs extending prediction-focused language models with tighter perception-action feedback loops may naturally embody contemplative wisdom as they scale

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• Reinforcement learning can be regarded as a limiting or special case of model-based approaches in general — or active inference in particular — when epistemic value is removed.claim0.797
  §3 Discussion.
• Friston, FitzGerald et al. (2016) — Active inference and learningconcept0.797
  Prior active inference paper providing detailed neurophysiological implementation of belief updates
• Active inference agents can learn epistemic habits in the absence of extrinsic rewards through ambiguity minimization.claim0.796
  §2, summarizing information-seeking behavior.
• Any system that exists will appear to minimize free energy and therefore engage in active inference. Furthermore, this apparently inferential or mindful behaviour is (almost) inevitable.concept0.793
  Friston's key assertion resolving the tautology: existence implies free energy minimization, making inference inevitable.
• "active inference separates the problems of optimizing action and perception by assuming that action fulfills predictions based on inferred states"quote0.792
• Active inference provides a principled account of epistemic exploration and intrinsic motivation as minimizing uncertainty.claim0.792
  §1, listing contributions.
• Active inference offers an attractive natural adaptation mechanism for non-stationary environments due to its Bayesian model updating properties.claim0.790
  §3, after non-stationary results.
• LLMs implicitly learn a distribution of 'consistent reasoning paths', and inconsistent reasoning forms statistical outliers with low probability under this distribution.hypothesis0.789
  Theoretical hypothesis about the mechanism underlying LLM error detection and reflection.