HOT-3: Agency guided by a general belief-formation system, with strong disposition to update beliefs per monitoring outputs

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

• HOT-1: Generative, top-down or noisy perception modulesconcept0.752
  Indicator: multiple sources of perceptual activity including top-down and noise required for reality monitoring.
• HOT-2: Metacognitive monitoring distinguishing reliable perceptual representations from noiseconcept0.741
  Indicator: reality monitoring mechanism assigning veracity probabilities.
• Glaese et al. 2022: Improving alignment of dialogue agents via targeted human judgementsconcept0.734
  Alignment paper cited as example of RLHF fine-tuning technique; ref 19
• Projections onto the Assistant Axis could serve as a real-time measure of model coherence in deployment—a quantitative signal for when models are drifting from their intended identityclaim0.728
  Proposed future application of the Assistant Axis
• Current application of 15 properties to agent harness is metaphorical; operationalization into measurement is an open empirical question.claim0.724
• Any system monitoring and controlling its own processing requires all three hierarchical levels: perception, attention, meta-awareness.claim0.721
  Design principle with implications for AI and consciousness-UX; architectural requirement for self-directed cognition.
• Under reward shaping (G=100, H=-100, F=0), Active Inference scored 99.52, Bayesian RL 99.77, Q-learning 95.56, with nearly identical behavior between belief-based agents.finding0.720
  Table 2, row 3, showing equivalence when prior preferences match rewards.
• Kim et al. 2025: Emergence of goal-directed behaviors via active inference with self-prior (arXiv:2504.11075)concept0.718
  Predecessor paper introducing the self-prior concept for goal-directed behavior emergence