A recursive loop that prioritizes internal model weights over new sensory input.
A metric that artificially inflates the model's certainty in its distorted outputs. 4. Preliminary Results
A mechanism that discards "contradictory" data points to maintain internal consistency.
#MachineLearning #CognitiveBias #Cybersecurity #RecursiveAI #DigitalPsychology zip configuration or the ethical implications?
The v0.1 release focuses on the . We utilize three primary modules:
We introduce , an experimental framework designed to analyze "machine delusion"—the phenomenon where deep learning models develop reinforced, self-validating feedback loops. Unlike standard hallucinations, which are transient, these delusions represent persistent structural biases within the model's latent space. This paper outlines the "default" configuration of the Deluded v0.1 engine, detailing its ability to simulate confirmation bias and overconfidence in predictive analytics. 2. Introduction