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arXiv AI By Jiahong Liu, Ming Shen, Xiaohao Liu, Rex Ying, Menglin Yang, Tat-Seng Chua, Irwin King

Hyperbolic Multimodal Continual Learning: A Closest-Admissible Solution

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The paper introduces Hyperbolic Multimodal Continual Learning (HMCL), a method that preserves the Lorentz geometry of hyperbolic multimodal models during sequential updates. By restricting all modalities to a shared hyperbolic isometry, HMCL formulates a joint closest‑admissible (CA) correction—along with a minimal‑rotation (MR) variant—to adjust AdamW updates while maintaining task performance. Experiments on a 16‑task classification‑retrieval stream with three hyperbolic backbones show that HMCL-CA achieves the highest overall score, reduces geometric drift by up to 95.5 %, and improves semantic hierarchy preservation on ImageNet‑WordNet. whyItMatters":"The study demonstrates that explicitly maintaining hyperbolic geometry during continual learning yields superior performance and reduced representation drift compared to existing baselines."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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