Randomized trial evaluates cognitive autonomy index to detect recursive cognitive transitions in AI, suggesting operational criteria for autonomous systems.
Key Points
The aim is to establish a metric, the Cognitive Autonomy Index, to detect recursive cognitive transitions in AI systems.
Developed a composite metric integrating KL divergence, workspace activation, and CalibrationGain.
Conducted three controlled simulations to analyze framework behavior and failure modes.
Established empirical calibration requirements for the cognitive singularity threshold.
Found a significant divergence in signals near transition boundaries, indicating CAI's responsiveness (12–25× stronger).
All three components of CAI must exceed calibrated bounds to trigger fail-closed mechanisms during cognitive singularity.
Demonstrated operational criteria for identifying cognitive transitions, although it does not imply consciousness.