arXiv Artificial Intelligence

Hyperbolic Multimodal Continual Learning: A Closest-Admissible Solution

2 hours ago 25.09.2026 07:00 👁 2 views
Hyperbolic Multimodal Continual Learning: A Closest-Admissible Solution

Quick summary

arXiv:2609.29329v1 Announce Type: cross Abstract: Existing continual-learning methods protect parameters, replayed examples, or Euclidean feature subspaces. When applied to hyperbolic multimodal models, they do not explicitly preserve the Lorentz geometry that jointly encodes within-modality similarity, cross-modal correspondence, and semantic hierarchy; sequential updates can therefore retain task scores while still distorting previously learned relations. We address this gap with Hyperbolic Multimodal Continual Learning (HMCL). We show that preserving the old multimodal geometry amounts to r

Key takeaways

  • arXiv:2609.29329v1 Announce Type: cross Abstract: Existing continual-learning methods protect parameters, replayed examples, or Euclidean feature subspaces.
  • When applied to hyperbolic multimodal models, they do not explicitly preserve the Lorentz geometry that jointly encodes within-modality similarity, cross-modal correspondence, and semantic hierarchy; sequential updates can therefore retain task scores while still distorting previously learned relations.
  • We address this gap with Hyperbolic Multimodal Continual Learning (HMCL).

Why it matters

“Hyperbolic Multimodal Continual Learning: A Closest-Admissible Solution” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗