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
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
Paper Title: Project Deluded: Quantifying Cognitive Distortions in Recursive Neural Architectures (v0.1) 1. Abstract We introduce , an experimental framework designed to
A mechanism that discards "contradictory" data points to maintain internal consistency. which are transient
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
A metric that artificially inflates the model's certainty in its distorted outputs. 4. Preliminary Results
#MachineLearning #CognitiveBias #Cybersecurity #RecursiveAI #DigitalPsychology zip configuration or the ethical implications?