The system simulates a sophisticated meta-cognitive AI that evolves its own architecture based on performance metrics, with beautiful visualizations showing the neural networks, architecture flows, and evolutionary progress in real-time.

We show that a model owner can artificially introduce uncertainty into their model and provide a corresponding detection mechanism.

Code for our paper analyzing the looseness of the upper bound on selective classification performance.

BoundaryBench: Benchmark + tool-augmented method for boundary containment under GPS noise

Investigation of how sampling strategies affect Selective Prediction performance in Multi Task Learning

Transform enrichment outputs into verifiable pathway claims via stability distillation, evidence modules, and mechanical PASS/ABSTAIN/FAIL audits.

Reproducible pipeline for silent-failure auditing in ECG accept-sets (MIT-BIH) with Newton–Puiseux onset scoring