AttenFeed unifies Attention and FFN into a single module. Building a ViT entirely from it (uViT) reveals that the rigid Attention–FFN split of standard ViTs is an inductive bias that hinders learning at smaller model scales.
Ph.D. Student · Yonsei University
Junhyeok Kim
Hello! 👋 I'm a Ph.D. student at MICV Lab at Yonsei University (Prof. Seong Jae Hwang). I'm currently interested in mechanistic interpretability, vision models, cognitive science, and a little bit of medical imaging!
- New My first first-author paper, which interprets ViT features and uncovers circuits to understand the model's underlying mechanisms, has been accepted to NeurIPS '25.
- Open I am actively looking for internship opportunities. Please feel free to reach out!
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Research
Currently, my primary research interest lies in Mechanistic Interpretability (MI). By leveraging MI, we can understand what capabilities an AI model specializes in and what capabilities it requires. I believe that this deep understanding of AI models will ultimately serve as a major foundation for advancing towards Artificial General Intelligence (AGI).
* equal contribution
SIGNAVOX is a framework that turns isolated sign clips into continuous 3D sign-language conversations and trains a model to generate sign responses directly from prior signing context, without relying on text at inference time.
vSTREAM enables real-time, faithful visual attribution streaming in multimodal reasoning models by amortizing causal effect estimation from attention features.
Two training-free approaches, Keyframe-anchored Attention Bias (KAB) and Rescaled Temporal RoPE (ReTRo), significantly enhances semantic fidelity, frame consistency, and pace stability in text-conditioned generative inbetweening.
To solve the problem of scarce adaptation data, the pre-training data of the backbone model can be selectively utilized to augment the adaptation dataset.
The image-to-text transfer in LVLMs depends on specialized attention head groups that are determined by the semantic content of the image.
Residual replacement model explains end-to-end decision-making process of vision transformers in human-understandable scale.
A few attention heads in frozen LVLMs demonstrate strong visual grounding capabilities. These "localization heads" immediately enable training-free detection and segmentation.
Large multimodal models consistently see irrelevant parts of the input. Our work demystifies this phenomenon, dubbed "visual attention sink," and proposes a simple mitigation strategy.
WoLF is a novel Large Language Model framework for Chest X-ray understanding that integrates patient Electronic Health Records (EHR).
Nothing here yet. Stay tuned!
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Miscellaneous
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I am currently the student leader of the MICV lab!
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Need another Junhyeok Kim? He's just one click away! (He is my mate as well as my namesake.)
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I'm the drummer at the MICCAI 2025 Gala Dinner!
