On the Spectral Geometry of Cross-Modal Representations: A Functional Map Diagnostic for Multimodal Alignment
We study cross-modal alignment between independently pretrained vision (DINOv2) and language (all-MiniLM-L6-v2) encoders using the functional map framework from computational geometry, which represents correspondence between representation manifolds as a compact linear operator between graph Laplacian eigenbases. While the framework underperforms Procrustes alignment and relative representations for cross-modal retrieval across all supervision budgets, it reveals a structural property of multimo
Record details
Published: 28 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 14 July 2026
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ethics.ai (28 March 2026), “On the Spectral Geometry of Cross-Modal Representations: A Functional Map Diagnostic for Multimodal Alignment,” evidence record 6647, https://ethics.ai/record/6647 (originally published by arXiv).
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