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
provides a baseline for understanding how software can "deceive" itself. Future iterations (v0.2 and beyond) will focus on "Intervention Protocols"—methods to break these self-reinforcing loops and restore objective processing. Suggested Tags / Keywords: Deluded_v0.1_default.zip
A mechanism that discards "contradictory" data points to maintain internal consistency. We introduce , an experimental framework designed to
#MachineLearning #CognitiveBias #Cybersecurity #RecursiveAI #DigitalPsychology zip configuration or the ethical implications? which are transient