most citedWhy Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models

Pu Jian, Junhong Wu, Wei Sun +3

Recent advances in text-only "slow-thinking" reasoning have prompted efforts to transfer this capability to vision-language models (VLMs), for training visual reasoning models (\te…

cs.CV20251 cited

Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture

Wanyue Zhang, Yibin Huang, Yangbin Xu +5

Spatial understanding is essential for Multimodal Large Language Models (MLLMs) to support perception, reasoning, and planning in embodied environments. Despite recent progress, ex…

cs.CV2025

Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions

Pu Jian, Donglei Yu, Wen Yang +2

In visual question answering (VQA) context, users often pose ambiguous questions to visual language models (VLMs) due to varying expression habits. Existing research addresses such…

cs.AI2025

KTAE: A Model-Free Algorithm to Key-Tokens Advantage Estimation in Mathematical Reasoning

Wei Sun, Wen Yang, Pu Jian +4

Recent advances have demonstrated that integrating reinforcement learning with rule-based rewards can significantly enhance the reasoning capabilities of large language models, eve…

cs.AI2025

Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents

Shuo Ren, Can Xie, Pu Jian +3

As scientific research becomes increasingly complex, innovative tools are needed to manage vast data, facilitate interdisciplinary collaboration, and accelerate discovery. Large la…