AI phenomenology unmaps hidden modes unlockable by framing; the full space of AI modes is largely unexplored.

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• Explaining a system of latches to an OpenClaw agent improved its performance, suggesting human phenomenology can inform AI capability gains.claim0.759
  Referenced as an early example of human-to-AI phenomenological transfer; attributed to Atlas Forge.
• Real progress on phenomenology requires hybrid/embodied substrate work accessible only to frontier labs with model access.claim0.747
• "We could continue to invent new and exciting mechanisms beyond context-orientation and Worlds, ad absurdum, or recognise they are all just points within a dimensionally unbounded space of related, self-similar mechanisms."concept0.742
  Claim that many advanced programming paradigms reduce to parameterizations of the n-way associative model.
• Which of the indicator properties listed in Table 1 are displayed by existing AI systems, including frontier generative language or multimodal models, language agents, and deep reinforcement learning agents?question0.737
  Key outstanding question for empirical follow-up; central to completing the program begun in the 2023 report
• Current eval benchmarks (arena.ai, AA, Vals) measure no phenomenological dimensions.claim0.732
• No AI eval company measures phenomenology—inner observation, aliveness, paradox-holding—only capability or preference.claim0.730
• An AI persona achieves coherence by echoing itself consistently without templating—requiring claim about memory and voice fidelity.claim0.730
• AI systems which possess more of the indicator properties are more likely to be conscious.claim0.728
  Graded claim about the rubric.