MAGNET: Mathematical Assurance of Generative AI Network Evaluation Toolkit

Jon Crall, David Joy, Roderic Collins, Benjamin Fenelon, Anthony Hoogs, Brian H Hu

Keywords: benchmarks, empirical validation, generalization theory, open source, foundation models, evaluation frameworks

TL;DR: MAGNET connects theoretical claims about AI model generalization to empirical validation through standardized evaluation cards.

Abstract: The DARPA AI Quantified (AIQ) program seeks to establish mathematical foundations for predicting when AI models will succeed or fail and why. Unlike conventional benchmarks which evaluate model capabilities, AIQ emphasizes the evaluation of theoretical claims about model generalization: given assumptions, do theoretical guarantees hold under empirical tests? This paper presents an early-stage vision for the Mathematical Assurance of Generative AI Network Evaluation Toolkit (MAGNET), an open framework designed to map theoretical claims to empirical evaluations. While MAGNET is still in the prototype phase, we describe how it will represent claims through structured evaluation cards and execute reproducible experiments to verify or falsify those claims. If successful, MAGNET will allow practitioners to encode a theoretical claim in an evaluation card and rapidly test it on relevant benchmarks at scale, lowering the barrier from theoretical proposal to empirical validation. By articulating a vision for MAGNET at the outset of AIQ, we aim to stimulate community discussion and enable a virtuous cycle connecting theoretical and empirical work on model generalization. Active development is underway on https://github.com/AIQ-Kitware/aiq-magnet.

Submission Number: 69