<ul><li>Backward compatible representation learning allows updated models to seamlessly integrate with existing ones, avoiding the need to reprocess stored data.</li><li>Existing compatibility approaches in Euclidean space overlook uncertainty in old embeddings and compel new models to reconstruct outdated representations regardless of their quality.</li><li>A new approach suggests using hyperbolic geometry, treating time as a natural axis to capture a model's confidence and evolution, maintaining generational consistency, and addressing uncertainties in representations.</li><li>Introduction of a robust contrastive alignment loss dynamically adjusts alignment weights based on uncertainty of old embeddings, with experiments showing the method's superiority in achieving compatibility for more resilient and adaptable machine learning systems.</li></ul>

Learning Along the Arrow of Time: Hyperbolic Geometry for Backward-Compatible Representation Learning

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