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
A recursive loop that prioritizes internal model weights over new sensory input. Deluded_v0.1_default.zip
Early testing on the v0.1 "default" set suggests that models with a "Deluded" architecture reach a state of 98% certainty on false premises within fewer than 500 iterations. We observe that once a "machine delusion" is established, traditional fine-tuning is often insufficient to rectify the bias. 5. Conclusion & Future Work We introduce , an experimental framework designed to
A metric that artificially inflates the model's certainty in its distorted outputs. 4. Preliminary Results We observe that once a "machine delusion" is
A mechanism that discards "contradictory" data points to maintain internal consistency.
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: