arXiv Computer Vision October 26, 2026 By Mingyang Gao, Yuwei Wu, Shiyu Luo, Wei Wu, Xiaomeng Fan, Qihao Lv, Haoyu Zhang, Rui Gao, Bingzhen Liu, Zhi Gao, Che Sun

Separating Angular Alignment from Radial Ordering in Hyperbolic Vision-Language Models

Read the original on arXiv Computer Vision →

The paper introduces AURORA, a hyperbolic vision‑language framework that separates angular alignment from radial ordering. Angular alignment captures semantic similarity while a novel Radial‑Angular Order Cone (RAOC) models hierarchical entailment based on parent‑child radial gaps. The approach includes norm regularization to prevent radial degeneration and is evaluated on large image‑text datasets, outperforming both Euclidean and hyperbolic baselines in zero‑shot classification, retrieval, hierarchical reasoning, and compositional understanding.

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 Computer Vision.

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