Deluded_v0.1_default.zip Apr 2026

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

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

#MachineLearning #CognitiveBias #Cybersecurity #RecursiveAI #DigitalPsychology zip configuration or the ethical implications? A metric that artificially inflates the model's certainty

Paper Title: Project Deluded: Quantifying Cognitive Distortions in Recursive Neural Architectures (v0.1) 1. Abstract The project aims to stress-test these systems by

A mechanism that discards "contradictory" data points to maintain internal consistency.

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

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