4 papers
See2Think: Do Multimodal Models Really Use Intermediate Visual States?
Siyu Yan, Zhuoran Yan, Haiying Xu +10
Multimodal large language models increasingly use sketches, annotations, tools, and intermediate images during reasoning, but it remains unclear whether they truly rely on these vi…
Latent Thought Flow: Efficient Latent Reasoning in Large Language Models
Xiandong Zou, Jing Huang, Jianshu Li +1
Large Language Models (LLMs) increasingly rely on intermediate reasoning, yet explicit Chain-of-Thought (CoT) suffers from a linguistic space bottleneck: each thought must be decod…
LOVA3: Learning to Visual Question Answering, Asking and Assessment
Henry Hengyuan Zhao, Pan Zhou, Difei Gao +2
Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge. By enhancing these capabilities, humans can mo…
Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator
Henry Hengyuan Zhao, Pan Zhou, Mike Zheng Shou
Multimodal Large Language Models (MLLMs) demonstrate exceptional problem-solving capabilities, but few research studies aim to gauge the ability to generate visual instruction tuni…