4 citations · 6 across the 6 of their papers we have counts for
7 papers
STORM: Internalized Modeling for Spatial-Temporal Reasoning in Video-Language Models
Yiming Liang, Yixiao Chen, Yiyang Zhou +8
Many video reasoning tasks require tracking motion, temporal order, and evolving visual states across frames. Existing methods built on large vision-language models (LVLMs) often a…
When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning
Shoubin Yu, Yue Zhang, Zun Wang +4
Despite rapid progress in MLLMs, visual spatial reasoning remains unreliable when correct answers depend on how a scene would appear under unseen or alternative viewpoints. Recent…
Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails
Siwei Han, Kaiwen Xiong, Jiaqi Liu +9
As Large Language Model (LLM) agents increasingly gain self-evolutionary capabilities to adapt and refine their strategies through real-world interaction, their long-term reliabili…
ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
Yiyang Zhou, Yangfan He, Yaofeng Su +5
Video understanding is fundamental to tasks such as action recognition, video reasoning, and robotic control. Early video understanding methods based on large vision-language model…
SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video Generation
Jaehong Yoon, Shoubin Yu, Vaidehi Patil +2
Recent advances in diffusion models have significantly enhanced their ability to generate high-quality images and videos, but they have also increased the risk of producing unsafe…
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences
Xiyao Wang, Yuhang Zhou, Xiaoyu Liu +9
Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks. However, current MLLM benchmarks are predominantly designed t…
Multimodal Representation Learning by Alternating Unimodal Adaptation
Xiaohui Zhang, Jaehong Yoon, Mohit Bansal +1
Multimodal learning, which integrates data from diverse sensory modes, plays a pivotal role in artificial intelligence. However, existing multimodal learning methods often struggle…