Persistent Convolution: A Topological Framework for AI Alignment Testing and Semantic Space Characterization
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 mode...
| Authors | Tyler Ashoff, Jordan Rodu |
|---|---|
| Primary category | stat.ML |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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