12 citations · 12 across the 7 of their papers we have counts for
9 papers
VAttnPO: Visual Attention-Guided Policy Optimization
Lihao Zheng, Zhenwei Shao, Zhou Yu +5
Multimodal reinforcement learning with verifiable rewards commonly assigns one sequence advantage to an entire response. This outcome-level signal distinguishes overall response qu…
Sibyl: An Efficient Small-large Model Collaboration Framework for Long-horizon Tasks
Zhewei Fang, Yuxin Zhang, Zhenwei Shao +8
Small language models (SLMs) offer a promising foundation for on-device agents through low-latency, resource-efficient inference, yet limited reasoning and planning capabilities co…
CGC: Compositional Grounded Contrast for Fine-Grained Multi-Image Understanding
Lihao Zheng, Zhenwei Shao, Yu Zhou +5
Although Multimodal Large Language Models (MLLMs) have advanced rapidly, they still face notable challenges in fine-grained multi-image understanding, often exhibiting spatial hall…
MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning
Jiawei Chen, Xintian Shen, Lihao Zheng +43
Traditional workflow-based agents exhibit limited intelligence when addressing real-world problems requiring tool invocation. Tool-integrated reasoning (TIR) agents capable of auto…
VideoARM: Agentic Reasoning over Hierarchical Memory for Long-Form Video Understanding
Yufei Yin, Qianke Meng, Minghao Chen +3
Long-form video understanding remains challenging due to the extended temporal structure and dense multimodal cues. Despite recent progress, many existing approaches still rely on…
MARS2 2025 Challenge on Multimodal Reasoning: Datasets, Methods, Results, Discussion, and Outlook
Peng Xu, Shengwu Xiong, Jiajun Zhang +125
This paper reviews the MARS2 2025 Challenge on Multimodal Reasoning. We aim to bring together different approaches in multimodal machine learning and LLMs via a large benchmark. We…
Growing a Multi-head Twig via Distillation and Reinforcement Learning to Accelerate Large Vision-Language Models
Zhenwei Shao, Mingyang Wang, Weijun Zhang +6
Large vision-language models (VLMs) have demonstrated remarkable capabilities in open-world multimodal understanding, yet their high computational overheads pose great challenges f…
Imp: Highly Capable Large Multimodal Models for Mobile Devices
Zhenwei Shao, Zhou Yu, Jun Yu +5
By harnessing the capabilities of large language models (LLMs), recent large multimodal models (LMMs) have shown remarkable versatility in open-world multimodal understanding. Neve…
Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering
Zhou Yu, Xuecheng Ouyang, Zhenwei Shao +2
Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowled…