RPT-1: Input modules using algorithmic recurrence

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

• RPT-2: Input modules generating organized, integrated perceptual representationsconcept0.827
  Indicator derived from recurrent processing theory; requires interpretability methods or behavioral tests like Kanizsa illusion
• RPT-2: Input modules generating organised, integrated perceptual representationsconcept0.822
  Indicator: perceptual organization beyond feature extraction.
• RPT-1 (algorithmic recurrence) may be useful as a sensitive indicator whose absence is strong evidence that a system is not conscious.claim0.809
  Illustrates how even simple AI properties can be informative as sensitive negative-direction indicators
• Does autoregressive use of transformer-based LLMs constitute algorithmic recurrence for RPT-1, or does it depend on where we draw the system boundary?question0.800
  Concrete interpretive challenge in applying RPT-1 indicator to current LLMs
• PP-1: Input modules using predictive codingconcept0.791
  Indicator from PP: use of predictive coding in input modules.
• Implementational recurrenceconcept0.754
  Recurrence via feedback loops where individual neurons process information repeatedly.
• autoregressive recurrenceconcept0.747
  Transformers are recurrent through autoregression because the K/V stream provides horizontal information flow across positions, even though each forward pass is feedforward.
• RL algorithmsconcept0.733
  The different reinforcement learning algorithms used across conditions, to ensure the alignment result is not algorithm-specific.