1 citations · 1 across the 8 of their papers we have counts for
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Evaluating Large Language Models on the 2026 Korean CSAT Mathematics Exam: Measuring Mathematical Ability in a Zero-Data-Leakage Setting
Goun Pyeon, Inbum Heo, Jeesu Jung +7
This study systematically evaluated the mathematical reasoning capabilities of Large Language Models (LLMs) using the 2026 Korean College Scholastic Ability Test (CSAT) Mathematics…
What Drives Paper Acceptance? A Process-Centric Analysis of Modern Peer Review
Sangkeun Jung, Goun Pyeon, Inbum Heo +1
Peer review is the primary mechanism for evaluating scientific contributions, yet prior studies have mostly examined paper features or external metadata in isolation. The emergence…
Reasoning Steps as Curriculum: Using Depth of Thought as a Difficulty Signal for Tuning LLMs
Jeesu Jung, Sangkeun Jung
Curriculum learning for training LLMs requires a difficulty signal that aligns with reasoning while remaining scalable and interpretable. We propose a simple premise: tasks that de…
LED Benchmark: Diagnosing Structural Layout Errors for Document Layout Analysis
Inbum Heo, Taewook Hwang, Jeesu Jung +1
Recent advancements in Document Layout Analysis through Large Language Models and Multimodal Models have significantly improved layout detection. However, despite these improvement…
ZEBRA: Leveraging Model-Behavioral Knowledge for Zero-Annotation Preference Dataset Construction
Jeesu Jung, Chanjun Park, Sangkeun Jung
Recent efforts in LLM alignment have focused on constructing large-scale preference datasets via human or Artificial Intelligence (AI) annotators. However, such approaches rely on…