SOURCE-LINKED INTELLIGENCE
Geometry-Aware Hyperbolic Residual Quantization
Residual Vector Quantization turns continuous representations into discrete, multi-level token sequences. Yet most methods operate in Euclidean space, despite the coarse-to-fine structure of the resulting codes and the latent hierarchies present in many data domains. Hyperbolic geometry offers a natural alternative for hierarchical representations, but naive hyperbolic extensions introduce geometric inconsistencies: non-associative hyperbolic addition prevents consistent residual aggregation, while standard straight-through gradient estimation ignores the geometry of the latent space. We propo
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-22T12:50:18.000Z
First collected: 2026-09-23T04:11:12.117Z. This is not the publication date.