Seungpil Lee

Gwangju Institute of Science and Technology (GIST)

Program: Master's in Artificial Intelligence

Email | Google Scholar | CV

Nice to meet you. I study AI in two directions — from AI back to the human mind it imitates, and from the mind forward into AI design. My current focus is the cognition of intelligent systems, studied through LLMs. Mechanistic interpretability (sparse autoencoders) keeps me honest about mechanisms, and memory-augmented agents that learn at test time are the current testbed. I came in through literature and philosophy of mind, so cognitive science and STS sit naturally alongside the engineering.

I am currently solving visual reasoning benchmark, Abstraction and Reasoning Corpus (ARC) with Prof. Sundong Kim in GIST. ARC is a kind of IQ test for computers - if we can solve this challenge, we might be able to develop AI with genuine reasoning capabilities. Perhaps you'll soon meet an AI that surpasses human intelligence. You might find my name there!

Education

Gwangju Institute of Science and Technology (GIST)

March 2025 - Present

• Master's Degree in AI Convergence
• Research focus: Cognition of Intelligent Systems via LLMs, Mechanistic Interpretability, Memory-Augmented Agents
• Advisor: Prof. Sundong Kim

Gwangju Institute of Science and Technology (GIST)

March 2018 - December 2024

• Bachelor's Degree in Electrical Engineering and Computer Science
• Minor in Mathematics
• Minor in Literature

Research Experience

Microsoft Research Asia (MSRA), Shanghai

March 2026 - August 2026

Research Intern, Collaborative Research Program

• Developing structured memory frameworks with test-time memory generation for self-evolving agents on compositional reasoning tasks

Data Science and Artificial Intelligence Lab, GIST

March 2024 - Present

Graduate Research Assistant, Supervised by Prof. Sundong Kim

• Working on Abstraction and Reasoning Corpus (ARC) challenge
• Developing novel approaches for visual reasoning and abstract problem solving
• Studying the cognition of intelligent systems through LLMs — failure modes, mechanistic interpretability, and memory-augmented agents that learn at test time

Publications

* denotes equal contribution

Journal Articles

Solution Augmentation for ARC-AGI Problems Using GFlowNet: A Probabilistic Exploration Approach

Sanha Hwang, Seungpil Lee, Sejin Kim, and Sundong Kim†

Transactions on Machine Learning Research, 2025

PAPER

Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus

Seungpil Lee*, Woochang Sim*, Donghyeon Shin*, Wongyu Seo, Jiwon Park, Seokki Lee, Sanha Hwang, Sejin Kim and Sundong Kim†

ACM Transactions on Intelligent Systems and Technology (Accept with Minor Revision), 2025

CODE PAPER SITE

@article{LoTH_TIST2024,
  title = {{Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus}},
  author = {Lee, Seungpil and Sim, Woochang and Shin, Donghyeon and Seo, Wongyu and Park, Jiwon and Lee, Seokki and Hwang, Sanha and Kim, Sejin and Kim, Sundong},
  journal = {ACM Transactions on Intelligent Systems and Technology (Accept with Minor Revision)},
  year = {2024},
}

Conference Papers

From Generation to Selection: Findings of Converting Analogical Problem-Solving into Multiple-Choice Questions

Donghyeon Shin*, Seungpil Lee*, Klea Lena Kovacec, and Sundong Kim†

In EMNLP Findings, 2024

CODE PAPER SITE

@article{Revisit_KAIS19,
  author = {Kim, Sundong and Lee, Jae-Gil},
  title = {{From Generation to Selection: Findings of Converting Analogical Problem-Solving into Multiple-Choice Questions}},
  author = {Shin, Donghyeon and Lee, Seungpil and Kovacec, Klea Lena and Kim, Sundong},
  booktitle = {EMNLP Findings},
  year = {2024},
  }

ARCLE: The Abstract and Reasoning Corpus Learning Environment for Reinforcement Learning

Hosung Lee*, Sejin Kim*, Seungpil Lee, Sanha Hwang, Jihwan Lee, Byung-Jun Lee†, and Sundong Kim†

In CoLLAs, 2024

CODE PAPER SITE

@article{arcle2024lee,
  author = {Hosung Lee, Sejin Kim, Seungpil Lee, Sanha Hwang, Jihwan Lee, Byung-Jun Lee, and Sundong Kim},
  title = {{ARCLE: The Abstraction and Reasoning Corpus Learning Environment for Reinforcement Learning}},
  author = {Lee, Hosung and Kim, Sejin and Lee, Seungpil and Hwang, Sanha and Lee, Jihwan and Lee, Byung-Jun and Kim, Sundong},
  booktitle = {CoLLAs},
  year = {2024}
}

Workshop Papers

Reasoning Abilities of Large Language Models through the Lens of Abstraction and Reasoning (Extended Abstract of the below paper)

Seungpil Lee*, Woochang Sim*, Donghyeon Shin*, Sejin Kim and Sundong Kim†

In NeurIPS Workshop on System-2 Reasoning at Scale, 2025

CODE PAPER SITE

@article{LoTHneurips2024Lee,
  title = {{Reasoning Abilities of Large Language Models through the Lens of Abstraction and Reasoning (Extended Abstract of the below paper)}},
  author = {Lee, Seungpil and Sim, Woochang and Shin, Donghyeon and Kim, Sejin and Kim, Sundong},
  booktitle = {NeurIPS Workshop on System-2 Reasoning at Scale},
  year = {2024},
  }

Regulation Using Large Language Models to Generate Synthetic Data for Evaluating Analogical Ability

Donghyeon Shin*, Seungpil Lee*, Klea Lena Kovacec, and Sundong Kim†

In IJCAI Workshop on Analogical Abstraction in Cognition, Perception, and Language, 2024

CODE PAPER SITE

@article{regulation2024shin,
  author = {Donghyeon Shin, Seungpil Lee, Klea Lena Kovacec, and Sundong Kim},
  title = {{Regulation Using Large Language Models to Generate Synthetic Data for Evaluating Analogical Ability}},
  author = {Shin, Donghyeon and Lee, Seungpil and Kovacec, Klea Lena and Kim, Sundong},
  booktitle = {IJCAI Workshop on Analogical Abstraction in Cognition, Perception, and Language},
  year = {2024},
  }

Preprints

From Noise to Diversity: Random Embedding Injection in LLM Reasoning

Heejun Kim*, Seungpil Lee*, Jewon Yeom, Jaewon Sok, Seonghyeon Park, Jeongjae Park, Taesup Kim†, Sundong Kim†

arXiv preprint, 2026

CODE PAPER

@article{RSP_arXiv2026,
  title = {{From Noise to Diversity: Random Embedding Injection in LLM Reasoning}},
  author = {Kim, Heejun and Lee, Seungpil and Yeom, Jewon and Sok, Jaewon and Park, Seonghyeon and Park, Jeongjae and Kim, Taesup and Kim, Sundong},
  journal = {arXiv preprint arXiv:2605.11936},
  year = {2026},
}

Can Large Language Models Develop Gambling Addiction?

Seungpil Lee*, Donghyeon Shin, Yunjeong Lee and Sundong Kim†

arXiv preprint, 2025

PAPER

@article{GamblingAddiction_arXiv2025,
  title = {{Can Large Language Models Develop Gambling Addiction?}},
  author = {Lee, Seungpil and Shin, Donghyeon and Lee, Yunjeong and Kim, Sundong},
  journal = {arXiv preprint},
  year = {2025},
}

Domestic Conference Papers

Extracting the core knowledge of ARC with the World Model

KSC, 2023

PAPER

Evaluating Prior Knowledge of ARC Using World Models

KSC, 2023

PAPER

Awards & Honors

National Research Foundation of Korea Funding, NRF

September 2025 - September 2026

• Funding offered to outstanding master's students for conducting research projects

Korean Government Scholarships, GIST College

March 2018 - August 2024

• Scholarship awarded to students studying in GIST

Scholarship for Summer Session Abroad

June 2019 - August 2019

• Scholarship awarded to students studying abroad during a summer session

Navy AI Competition

June 2023 - August 2023

• Awarded drone object detecting competition hosted by Korean Navy

K-StartUp finals

March 2023 - November 2023

• Advanced to the finals of Korean largest start-up contest, 'K-StartUp', as ML researcher and back-end developer

Media Coverage

Can AI become a tool for safer gambling?

SiGMA News, 2026

ARTICLE

AI Bots Show Signs of Gambling Addiction, Study Finds

Newsweek, 2026

ARTICLE

AI can show gambling addiction-like behaviour under risk: Expert

SiGMA News, 2026

ARTICLE

New Data Augmentation Method Improves ARC Accuracy by 5.85%p

Dong-A Science, 2025

ARTICLE

Quantitative Evaluation Framework for LLM Reasoning Abilities

News1, 2025

ARTICLE