A metric that artificially inflates the model's certainty in its distorted outputs. 4. Preliminary Results
As AI systems become increasingly recursive, the risk of "epistemic closure" grows. The project aims to stress-test these systems by intentionally introducing "seed delusions" (contained in the default.zip configuration) to observe how quickly a model diverges from objective ground-truth data. 3. Methodology: The "Default" Environment Deluded_v0.1_default.zip
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 metric that artificially inflates the model's certainty
#MachineLearning #CognitiveBias #Cybersecurity #RecursiveAI #DigitalPsychology zip configuration or the ethical implications? The project aims to stress-test these systems by
A mechanism that discards "contradictory" data points to maintain internal consistency.
The v0.1 release focuses on the . We utilize three primary modules:
A recursive loop that prioritizes internal model weights over new sensory input.