What new metrics are needed to evaluate whether an AI truly exhibits a wise world model?

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

• The right scale-set for measuring AI aliveness must be chosen: word/sentence/response, message/exchange/conversation, or lifetime relationship.claim0.782
• Many of the indicator properties can be implemented in AI systems using current techniques.claim0.766
  Feasibility demonstrated in Section 3.1.
• If an AI system could be a welfare subject and moral patient, then many model instances could be run after training, scaling up the problem rapidly.hypothesis0.763
  Scalability concern.
• World models in natural and artificial intelligence (theme issue)concept0.760
  2026 theme issue; this paper is the flagship integrative contribution.
• No AI eval company measures phenomenology—inner observation, aliveness, paradox-holding—only capability or preference.claim0.755
• Patterns in AI self-reports should be compared across different models to identify structural commonalities.claim0.755
• AI systems need to be able to deal with reality as it actually is, not with the way that we think it is.claim0.755
  Paraphrase of Cantwell Smith's argument; aligns with Buddhist emphasis on seeing reality without conceptual imposition.
• World models provide a bridge between diverse substrates of intelligence—biological, artificial, and hybrid.claim0.754
  Synthetic claim integrating Ha's work with Levin's xenobiology and Tenenbaum's cognitive modeling.