5 papers
VIVA+: Human-Centered Situational Decision-Making
Zhe Hu, Yixiao Ren, Guanzhong Liu +2
Multimodal Large Language Models (MLLMs) show promising results for embodied agents in operating meaningfully in complex, human-centered environments. Yet, evaluating their capacit…
Tool-R1: Sample-Efficient Reinforcement Learning for Agentic Tool Use
Yabo Zhang, Yihan Zeng, Qingyun Li +3
Large language models (LLMs) have demonstrated strong capabilities in language understanding and reasoning, yet they remain limited when tackling real-world tasks that require up-t…
When 'YES' Meets 'BUT': Can Large Models Comprehend Contradictory Humor Through Comparative Reasoning?
Tuo Liang, Zhe Hu, Jing Li +8
Understanding humor-particularly when it involves complex, contradictory narratives that require comparative reasoning-remains a significant challenge for large vision-language mod…
Praxis-VLM: Vision-Grounded Decision Making via Text-Driven Reinforcement Learning
Zhe Hu, Jing Li, Zhongzhu Pu +2
Vision Language Models exhibit impressive performance for various tasks, yet they often lack the sophisticated situational reasoning required for complex decision-making. This pape…
Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
Shuhao Gu, Jialing Zhang, Siyuan Zhou +23
Recently, Vision-Language Models (VLMs) have achieved remarkable progress in multimodal tasks, and multimodal instruction data serves as the foundation for enhancing VLM capabiliti…