collaborators

6 papers

cs.SE2026

Dashboard2Code: Evaluating Multimodal Models on Reconstructing Interactive Dashboards

Tianhao Niu, Ziyu Han, Qiguang Chen +5

Automatic data visualization generation has advanced rapidly with multi-modal large language models, yet existing efforts largely focus on static charts and overlook the interactiv…

cs.CL2026

What You See Is What You Get: Observation-Aligned Supervision for Chart-to-Code Generation

Tianhao Niu, Qingfu Zhu, Wanxiang Che

Chart-to-code generation is commonly trained with supervised fine-tuning on reference plotting scripts, implicitly treating the gold code as a fully observable target. We argue tha…

cs.CL2026

When Does Language Matter? Multilingual Instructions Reveal Step-wise Language Sensitivity in Vision-Language-Action Models

Xuan Dong, Zhe Han, Tianhao Niu +2

Vision-Language-Action (VLA) models have shown strong performance in language-conditioned robotic manipulation, yet their robustness to linguistic variation remains poorly understo…

cs.CV2026

ChartREG++: Towards Benchmarking and Improving Chart Referring Expression Grounding under Diverse referring clues and Multi-Target Referring

Tianhao Niu, Ziyu Han, Xuan Dong +2

Referring expression grounding is a core problem in visual grounding and is widely used as a diagnostic of spatial grounding and reasoning in vision and language models, yet most p…

cs.CL2026

EpiBench: Benchmarking Multi-turn Research Workflows for Multimodal Agents

Xuan Dong, Huanyang Zheng, Tianhao Niu +7

Scientific research follows multi-turn, multi-step workflows that require proactively searching the literature, consulting figures and tables, and integrating evidence across paper…

cs.CV2024

Exploring Multi-Grained Concept Annotations for Multimodal Large Language Models

Xiao Xu, Tianhao Niu, Yuxi Xie +3

Multimodal Large Language Models (MLLMs) excel in vision--language tasks by pre-training solely on coarse-grained concept annotations (e.g., image captions). We hypothesize that in…