The direction of optimal inference for the post-dual agent is to see the world as the world sees it

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• post-dual agentconcept0.780
  An agent that has eliminated the self-environment partition prior and deploys QRFs without that constraint.
• Active inference agents can carry out epistemic exploration and account for uncertainty about their environment in a Bayes-optimal fashion.claim0.776
  Abstract and §1, summarizing a key property.
• Active inference achieves Bayes-optimal behavior in non-stationary environments through online belief updating.hypothesis0.771
  Tested via FrozenLake experiments; predicts superior performance when environment dynamics change.
• Active inference agents can learn their own reward function (prior preferences) by interacting with the environment, bypassing the need for an explicit reward signal.claim0.770
  Abstract and §3, preference learning section.
• Under active inference, the ultimate ‘goal’ is to maintain a coherent phenotype and persist over time, not to maximize reward.claim0.770
  §3, preference learning discussion.
• No single layer is universally optimal for probing truth directions; different tasks peak at different layers.claim0.764
  Argues against the single-layer analysis approach of prior work.
• Emptiness realisation and compassion practices are jointly necessary for post-dual optimal inference: emptiness removes the constraint; compassion orients the agent toward QRF alignmenthypothesis0.763
  Formal account of why Buddhist traditions present emptiness and compassion as inseparable
• We hypothesize that explicitly instructing the model to evaluate the correctness of the given statement may change the geometry of truth directions.hypothesis0.756
  Motivating hypothesis for Section 5's investigation of prompt template effects.