Diffusion · Language Models · Inner Workings

Injin Kong

Rethinking language generation through diffusion and inner mechanisms.

I am a master's student in Data Science at Seoul National University and a member of HOLI LAB. I received my B.S. in Mathematics from Seoul National University. My research focuses on diffusion-based language generation, the internal mechanisms of language models, and reliable AI applications.

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About

Understanding AI systems from the inside out.

I am a master's student in Data Science at Seoul National University and a member of HOLI LAB. I received my B.S. in Mathematics from Seoul National University.

My research studies the internal mechanisms of language models and alternative approaches to generation, especially diffusion-based language modeling. I am interested in how models represent information, how their behavior changes through training and post-training, and how these insights can lead to more reliable AI systems.

Research Interests

Language models, inner workings, and diffusion-based generation.

Inner Workings of Language Models

Understanding how language models represent information, organize internal computation, and change their mechanisms through training, post-training, and adaptation.

Diffusion-based Language Generation

Exploring diffusion-based generative paradigms for language, including masked diffusion models, decoding dynamics, and hidden-state diffusion interfaces.

AI Agents and Applications

Building reliable language-model-based systems that connect research ideas to real-world workflows, educational tools, and agentic applications.

Selected Papers

Research output and manuscripts.

Where Should Diffusion Enter a Language Model? Geometry-Guided Hidden-State Replacement

Injin Kong, Hyoungjoon Lee, Yohan Jo · ICML FoGen Workshop 2026

Studies where diffusion mechanisms should enter language models through geometry-guided hidden-state replacement.

arXiv

Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models

Jongwook Han*, Jongwon Lim*, Injin Kong, Yohan Jo · ICML 2026

Investigates how large language models express values through intrinsic behavior and prompt-induced responses.

arXiv

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Can MLLMs Reason About Visual Persuasion? Evaluating the Efficacy and Faithfulness of Reasoning

Naeun Lee, Hyunjong Kim, Sunghwan Choi, Injin Kong, Yohan Jo · arXiv preprint · 2026

Evaluates whether multimodal large language models can reason effectively and faithfully about visual persuasion.

arXiv

Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models

Injin Kong*, Hyoungjoon Lee*, Yohan Jo · arXiv preprint · 2026

Studies how model mechanisms shift during post-training from autoregressive language models to masked diffusion language models.

arXiv

Style Extraction on Text Embeddings Using VAE and Parallel Dataset

Injin Kong, Shinyee Kang, Yuna Park, Sooyong Kim, Sanghyun Park · arXiv preprint · 2024

Proposes a VAE-based approach for extracting style information from text embeddings using a parallel dataset.

arXiv

Education, awards, and experience.

Education

2025 - Present Seoul National University

M.S. in Data Science

2020 - 2025 Seoul National University

B.S. in Mathematics

2019 Yonsei University

Withdrew to enroll in Seoul National University

2016 - 2018 Hana Academy Seoul

High School

Awards

2023 Second Place
CJ Logistics Future Technology Challenge · Image-Based Volume Estimation of Parcels
2019 Academic Excellence Award
Yonsei University · Highest Academic Achievement
2018 Gold Prize
Korean Mathematical Olympiad (KMO)
2018 Grand Prize (1st Place)
Earth Science Competition · Hana Academy Seoul
2018 Gold Prize (2nd Place)
Mathematics Research Presentation Contest · Hana Academy Seoul

Experience

2023.11 - 2025.08 Chief Science Officer

Aardvark

2019 Proofreading Committee Member

Black Label Geometry

Contact

Feel free to contact me.

For research discussions, collaboration, or opportunities, feel free to reach out by email.

mtkong77@gmail.com Google Scholar CV PDF