RPT-2: Input modules generating organised, integrated perceptual representations

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 recurrenceconcept0.822
  Indicator derived from RPT: use of algorithmic recurrence in input modules.
• PP-1: Input modules using predictive codingconcept0.753
  Indicator from PP: use of predictive coding in input modules.
• RPT-1 (algorithmic recurrence) may be useful as a sensitive indicator whose absence is strong evidence that a system is not conscious.claim0.743
  Illustrates how even simple AI properties can be informative as sensitive negative-direction indicators
• Hypothesis 2 (Persona Space): Persona vectors jointly compose a persona space; an instance's general dispositional profile is specified by a combination of activations along multiple persona vectorshypothesis0.740
  Second of three hypotheses about persona implementation, supported by PCA evidence from Lu et al.
• HOT-2: Metacognitive monitoring distinguishing reliable perceptual representations from noiseconcept0.739
  Indicator: reality monitoring mechanism assigning veracity probabilities.
• Incorporating machine learning provides objective standards that help mitigate subjectivity in emergence identification.claim0.729
  Authors argue ML optimizers act as objective observers.
• A linear preference vector encoding how much a model likes a given task is persona-relative: it activates strongly for creative writing in the assistant but for phishing in the evil personafinding0.729
  Evidence that core representations like preferences are persona-relative, supporting claim that personas gate content of representations
• Precise characterization of the perceptual representational format — specific, sustained, globally constitutive — remains an open problemconcept0.725
  Acknowledged gap in CIMC's theory: the exact computational description of the perceptual regime is not yet specified