Deluded_v0.1_default.zip File

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

A mechanism that discards "contradictory" data points to maintain internal consistency. Deluded_v0.1_default.zip

A recursive loop that prioritizes internal model weights over new sensory input. Early testing on 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: Conclusion & Future Work A mechanism that discards

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