A recursive loop that prioritizes internal model weights over new sensory input.
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:
A mechanism that discards "contradictory" data points to maintain internal consistency.
#MachineLearning #CognitiveBias #Cybersecurity #RecursiveAI #DigitalPsychology zip configuration or the ethical implications?
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
A metric that artificially inflates the model's certainty in its distorted outputs. 4. Preliminary Results
The v0.1 release focuses on the . We utilize three primary modules:
