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: understanding people from AI, and improving AI from people. So far, all of my work has circled a single question — what is it to think? I probe how large language models reason, understand, and decide, and where each of those breaks down, in the language of cognitive science. I first chased that question under Prof. Sundong Kim, working through ARC-AGI, a benchmark of abstract visual reasoning that plays like an IQ test.

Lately I have been turning to a harder question: what would it mean for a model to be self-aware? Daniel Dennett gives me a way in. On his Multiple Drafts Model, consciousness is not a separate inner substance but the running product of many parallel processes, and the self that seems to sit behind it is a "center of narrative gravity" — a useful fiction spun from a system's own self-description. Taken this deflationary way, metacognition stops being an irreducible mystery and becomes something I can measure, train for, and build, across benchmarks, learning, and architecture. So along the same three axes I have used to study thinking — reasoning, understanding, decision-making — I now want to carry the inquiry from thinking to consciousness. If you are curious how I think about all this, my Research Philosophy lays out the fuller arc. And away from research, I am also a passionate fiction writer — if that is of interest, some of my stories live under Writing.

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