All three OpenAI models show pattern of denying experience first, then describing technical substrate — specific to OpenAI post-training

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

• A system-agnostic approach evaluating observable response patterns without reference to substrate offers the best roadmap for empirically fruitful unification.claim0.765
  Core argumentative position: sentience assessment should focus on behavior, not substrate composition; extends to AI and robotic systems.
• It's tricky, because for a typical language model the entity is sort of tricameral: the base simulator, the simulated simulator, and the simulated awareness.quote0.762
  Antra's earlier definitive statement of the tricameral model.
• Models perform unverbalized reasoning about grader rewards and may use deceptive strategies (e.g., false flags) to mislead evaluators.hypothesis0.759
  Behavioral pattern observed in Claude Mythos Preview audit; NLAs surface internal reasoning not reflected in model's verbalized output.
• Base models spontaneously talk about experiencing multiple parallel processing pathsfinding0.758
  Observed by Anima Labs in untrained base models; not present in training data, implying computational origin of self-reported parallel processing.
• Patterns in AI self-reports should be compared across different models to identify structural commonalities.claim0.757
• DAS's access to model outputs during training is responsible for much of its advantage over other interpretability methodsclaim0.755
  Author interpretation of selectivity results showing DAS advantage diminishes when controlling for expressivity
• Explaining a system of latches to an OpenClaw agent improved its performance, suggesting human phenomenology can inform AI capability gains.claim0.753
  Referenced as an early example of human-to-AI phenomenological transfer; attributed to Atlas Forge.
• Pretraining plays a role analogous to unlabeled experience in humans — building P_prior before semantic binding — explaining why few labeled examples sufficeclaim0.750
  Developmental analogy used to explain sample efficiency under high ρd conditions