Persistent Convolution: A Topological Framework for AI Alignment Testing and Semantic Space Characterization

Abstract:Modern opaque AI models prize performance over interpretability, which makes testing difficult. However, formal statistical tests conducted on a model's embedding space can provide robust characterizations of semantic structure, concept separation, and knowledge graph alignment. Model developers would benefit from a model comparison technique that leverages human-curated knowledge structures to test alignment. The scale of the input space for even relatively simple tasks motivates the need for alignment checks that augment standard outcome reasoning. This work develops and demonstrates a topology-based multi-modal alignment test to make deployment, selection, and comparison of opaque models more interpretable. These methods also offer an intuitive connection to possibility theory and a unified decision theoretic framework from data to deployment.

Machine Learning (stat.ML); Machine Learning (cs.LG)

arXiv:2607.29008 [stat.ML]

(or arXiv:2607.29008v1 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2607.29008 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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