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 recursive loop that prioritizes internal model weights over new sensory input. Deluded_v0.1_default.zip

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: We introduce , an experimental framework designed to

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 Future iterations (v0

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

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: