1 citations · 1 across the 8 of their papers we have counts for
8 papers
Chain Of Interaction Benchmark (COIN): When Reasoning meets Embodied Interaction
Xianhao Wang, Xiaojian Ma, Haozhe Hu +7
Generalist embodied agents must perform interactive, causally-dependent reasoning, continually interacting with the environment, acquiring information, and updating plans to solve…
AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning
Mingyang Song, Haoyu Sun, Jiawei Gu +4
When humans face problems beyond their immediate capabilities, they rely on tools, providing a promising paradigm for improving visual reasoning in multimodal large language models…
ThinkMorph: Emergent Properties in Multimodal Interleaved Chain-of-Thought Reasoning
Jiawei Gu, Yunzhuo Hao, Huichen Will Wang +5
Multimodal reasoning requires iterative coordination between language and vision, yet it remains unclear what constitutes a meaningful interleaved chain of thought. We posit that t…
Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers
Zhaochen Su, Peng Xia, Hangyu Guo +12
Recent progress in multimodal reasoning has been significantly advanced by textual Chain-of-Thought (CoT), a paradigm where models conduct reasoning within language. This text-cent…
FullFront: Benchmarking MLLMs Across the Full Front-End Engineering Workflow
Haoyu Sun, Huichen Will Wang, Jiawei Gu +2
Front-end engineering involves a complex workflow where engineers conceptualize designs, translate them into code, and iteratively refine the implementation. While recent benchmark…
OpenThinkIMG: Learning to Think with Images via Visual Tool Reinforcement Learning
Zhaochen Su, Linjie Li, Mingyang Song +8
While humans can flexibly leverage interactive visual cognition for complex problem-solving, enabling Large Vision-Language Models (LVLMs) to learn similarly adaptive behaviors wit…
ImageGen-CoT: Enhancing Text-to-Image In-context Learning with Chain-of-Thought Reasoning
Jiaqi Liao, Zhengyuan Yang, Linjie Li +4
In this work, we study the problem of Text-to-Image In-Context Learning (T2I-ICL). While Unified Multimodal LLMs (MLLMs) have advanced rapidly in recent years, they struggle with c…
Can MLLMs Reason in Multimodality? EMMA: An Enhanced MultiModal ReAsoning Benchmark
Yunzhuo Hao, Jiawei Gu, Huichen Will Wang +4
The ability to organically reason over and with both text and images is a pillar of human intelligence, yet the ability of Multimodal Large Language Models (MLLMs) to perform such…