Deluded_v0.1_default.zip Apr 2026

A recursive loop that prioritizes internal model weights over new sensory input.

A metric that artificially inflates the model's certainty in its distorted outputs. 4. Preliminary Results Deluded_v0.1_default.zip

The v0.1 release focuses on the . We utilize three primary modules: A recursive loop that prioritizes internal model weights

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 Preliminary Results The v0

Paper Title: Project Deluded: Quantifying Cognitive Distortions in Recursive Neural Architectures (v0.1) 1. Abstract

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:

#MachineLearning #CognitiveBias #Cybersecurity #RecursiveAI #DigitalPsychology zip configuration or the ethical implications?