ICS-RPT (Intrinsic Causal-Structure reading of RPT)

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 organised, integrated perceptual representationsconcept0.740
  Indicator: perceptual organization beyond feature extraction.
• RPT-2: Input modules generating organized, integrated perceptual representationsconcept0.738
  Indicator derived from recurrent processing theory; requires interpretability methods or behavioral tests like Kanizsa illusion
• RPT-1: Input modules using algorithmic recurrenceconcept0.726
  Indicator derived from RPT: use of algorithmic recurrence in input modules.
• Causal Structural Probemethod0.721
  Probe method combining causal interventions and structural analysis, supported by pyvene's activation collection
• RPT-1 (algorithmic recurrence) may be useful as a sensitive indicator whose absence is strong evidence that a system is not conscious.claim0.716
  Illustrates how even simple AI properties can be informative as sensitive negative-direction indicators
• UCCT strictly generalizes ICL and reads retrieval-augmented generation and fine-tuning as the same anchoring process acting on one measurable score Sclaim0.706
  Authors' central interpretive claim about the scope of their theory
• CF-IIT (Computational Functionalist reading of IIT)framework0.698
  This paper's constructed computational functionalist analogue of IIT, positioned at Level 2.
• Causal Representation Learning (CRL)framework0.694
  Schölkopf et al.'s framework combining representation learning with causal inference.