RPT-1 (algorithmic recurrence) may be useful as a sensitive indicator whose absence is strong evidence that a system is not conscious.

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

• RPT-1: Input modules using algorithmic recurrenceconcept0.809
  Indicator derived from RPT: use of algorithmic recurrence in input modules.
• Algorithmic recurrence is likely necessary for conscious experience with human-like temporal character.claim0.804
  Support for RPT-1.
• Recurrent Processing Theory (RPT)framework0.790
  A neuroscientific theory claiming that recurrent processing in perceptual areas is necessary and sufficient for conscious vision.
• Does autoregressive use of transformer-based LLMs constitute algorithmic recurrence for RPT-1, or does it depend on where we draw the system boundary?question0.783
  Concrete interpretive challenge in applying RPT-1 indicator to current LLMs
• Satisfying the indicators would not mean that an AI system would definitely be conscious.claim0.770
  Caveat that indicators are not conclusive proof.
• Superficial behavioral indicators of consciousness are especially vulnerable to the gaming problem in AI contexts because engineers can design non-conscious systems that manifest them.claim0.755
  Further motivation for preferring internal computational indicators over behavioral ones
• RPT-2: Input modules generating organized, integrated perceptual representationsconcept0.750
  Indicator derived from recurrent processing theory; requires interpretability methods or behavioral tests like Kanizsa illusion
• AI systems which possess more of the indicator properties are more likely to be conscious.claim0.744
  Graded claim about the rubric.