18 citations · 31 across the 27 of their papers we have counts for
4 papers · 2 filters
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…
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…