Hyunmin Song — accepted AI-conference papers

2025

RingFormer: Rethinking Recurrent Transformer with Adaptive Level Signals

Jaemu Heo, Eldor Fozilov, Hyunmin Song, Taehwan Kim

EMNLP 2025

Transformers have achieved great success in effectively processing sequential data such as text. Their architecture consisting of several attention and feedforward blocks can model relations between elements of a sequence in parallel manner, which makes them very efficient to train and effective in

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2025

VEHME: A Vision-Language Model For Evaluating Handwritten Mathematics Expressions

Thu Phuong Nguyen, Duc M. Nguyen, Hyotaek Jeon, Hyunwook Lee, Hyunmin Song, Sungahn Ko +1

EMNLP 2025

Automatically assessing handwritten mathematical solutions is an important problem in educational technology with practical applications, but remains a significant challenge due to the diverse formats, unstructured layouts, and symbolic complexity of student work. To address this challenge, we intro

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