Deluded_v0.1_default.zip May 2026

The v0.1 release focuses on the . We utilize three primary modules:

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 Deluded_v0.1_default.zip

A recursive loop that prioritizes internal model weights over new sensory input. The v0

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: This paper outlines the "default" configuration of the

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

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