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Guannan Zhang

21 papers hereh-index 214 citations6 works total

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

author position
  • middle author11
  • last author10

Across the 21 of 21 papers where every author was matched, so the position is known.

fields
  • cs.AI6
  • cs.CV4
  • cs.IR4
  • cs.CL3
  • cs.LG3
  • cs.RO1
same name
  • Guannan Zhang — 38 papers, h 7
  • Guannan Zhang — 22 papers, h 24
  • Guannan Zhang — 11 papers, h 22
  • Guannan Zhang — 5 papers, h 3
  • Guannan Zhang — 4 papers, h 4
  • Guannan Zhang — 3 papers, h 3

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
20232026
most citedOn the Opportunities of Green Computing: A Survey

9 citations · 21 across the 20 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

AdaVDR: Adaptive Tool Use and Reflection for Video Deep Research

Xintong Zhang, Xiaomeng Fan, Shilin Yan +7

Video deep research aims to answer questions by jointly understanding video content and retrieving external knowledge from the open Web. However, these tasks often require diverse…

cs.CV2026

Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models

Shilin Yan, Jintao Tong, Hongwei Xue +6

The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a profound meta-cognitive deficit…

cs.CV2026

MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning

Haozhan Shen, Shilin Yan, Hongwei Xue +5

Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step depends on verified visual compositional c…

cs.CV2026

SwimBird: Eliciting Switchable Reasoning Mode in Hybrid Autoregressive MLLMs

Jintao Tong, Shilin Yan, Hongwei Xue +5

Multimodal Large Language Models (MLLMs) have made remarkable progress in multimodal perception and reasoning by bridging vision and language. However, most existing MLLMs perform…

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