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What MLLMs Learn about When they Learn about Multimodal Reasoning
Jiwan Chung, Neel Joshi, Pratyusha Sharma +2
Evaluation of multimodal reasoning models is typically reduced to a single accuracy score, implicitly treating reasoning as a unitary capability. We introduce MathLens, a benchmark…
Are Any-to-Any Models More Consistent Across Modality Transfers Than Specialists?
Jiwan Chung, Janghan Yoon, Junhyeong Park +4
Any-to-any generative models aim to enable seamless interpretation and generation across multiple modalities within a unified framework, yet their ability to preserve relationships…
v1: Learning to Point Visual Tokens for Multimodal Grounded Reasoning
Jiwan Chung, Junhyeok Kim, Siyeol Kim +3
When thinking with images, humans rarely rely on a single glance: they revisit visual evidence while reasoning. In contrast, most Multimodal Language Models encode an image once to…
Do MLLMs Capture How Interfaces Guide User Behavior? A Benchmark for Multimodal UI/UX Design Understanding
Jaehyun Jeon, Min Soo Kim, Jang Han Yoon +5
User interface (UI) design goes beyond visuals to shape user experience (UX), underscoring the shift toward UI/UX as a unified concept. While recent studies have explored UI evalua…
Explain with Visual Keypoints Like a Real Mentor! A Benchmark for Multimodal Solution Explanation
Jaewoo Park, Jungyang Park, Dongju Jang +6
With the rapid advancement of mathematical reasoning capabilities in Large Language Models (LLMs), AI systems are increasingly being adopted in educational settings to support stud…
Selective Vision is the Challenge for Visual Reasoning: A Benchmark for Visual Argument Understanding
Jiwan Chung, Sungjae Lee, Minseo Kim +4
Visual arguments, often used in advertising or social causes, rely on images to persuade viewers to do or believe something. Understanding these arguments requires selective vision…