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Hang Zhao

9 papers hereh-index 5556 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author2
  • last author6

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.RO4
  • cs.LG1
same name
  • Hang Zhao — 31 papers, h 32
  • Hang Zhao — 24 papers, h 15
  • Hang Zhao — 16 papers, h 11
  • Hang Zhao — 10 papers, h 4
  • Hang Zhao — 9 papers, h 5
  • Hang Zhao — 9 papers, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedDriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models

23 citations · 23 across the 9 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.LG2026

Structured-Sparse Attention for Entity Tracking with Subquadratic Sequence Complexity

Hangyue Zhao, Paul Caillon, Erwan Fagnou +1

Entity tracking requires maintaining and updating latent states for entities and attributes over long sequences. Recent task-specific attention operators can compress deep Transfor…

cs.CV2026

DriveMA: Rethinking Language Interfaces in Driving VLAs with One-Step Meta-Actions

Weicheng Zheng, Yixin Huang, Qiao Sun +2

Driving Vision-Language-Action Models (Driving VLAs) commonly introduce natural-language reasoning as an intermediate interface for end-to-end planning, but reasoning-centric inter…

cs.RO2026

Dexora: Open-source VLA for High-DoF Bimanual Dexterity

Zongzheng Zhang, Jingrui Pang, Zhuo Yang +22

Vision-Language-Action (VLA) models have recently become a central direction in embodied AI, but current systems are restricted to either dual-gripper control or single-arm dextero…

cs.CV2026

DriveMA: Driving Vision-Language-Action Models with verifiable Meta-Actions

Weicheng Zheng, Yixin Huang, Qiao Sun +2

Driving Vision-Language-Action Models (Driving VLAs) aim to use language to improve end-to-end planning, but the language-action gap limits this promise. We propose DriveMA, a Driv…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.