13 papers
When the Strike Zone Becomes Algorithmic: Umpire Judgment and Player Challenge Decisions under AI Review
Kichang Lee, Gyeongmin Han, Sungmin Lee +1
The Automated Ball-Strike challenge system that Major League Baseball adopted in 2026 offers a distinctive setting for studying human AI interaction in which umpires make every bal…
Motif-Video 2B: Technical Report
Junghwan Lim, Wai Ting Cheung, Minsu Ha +25
Training strong video generation models usually requires massive datasets, large parameter counts, and substantial compute. In this work, we ask whether strong text-to-video qualit…
LLMs Have Made Failure Worth Publishing
Sungmin Lee
Scientific publishing systematically filters out negative results. We argue that this long-standing asymmetry has become an urgent problem in the era of large language models, whic…
Spatial Discretization for Fine-Grain Zone Checks with STARKs
Sungmin Lee, Kichang Lee, Gyeongmin Han +1
Many location-based services rely on a point-in-polygon test (PiP), checking whether a point or a trajectory lies inside a geographic zone. Since geometric operations are expensive…
Verifiable Dropout: Turning Randomness into a Verifiable Claim
Kichang Lee, Sungmin Lee, Jaeho Jin +1
Modern cloud-based AI training relies on extensive telemetry and logs to ensure accountability. While these audit trails enable retrospective inspection, they struggle to address t…
Motif-2-12.7B-Reasoning: A Practitioner's Guide to RL Training Recipes
Junghwan Lim, Sungmin Lee, Dongseok Kim +23
We introduce Motif-2-12.7B-Reasoning, a 12.7B parameter language model designed to bridge the gap between open-weight systems and proprietary frontier models in complex reasoning a…
Motif 2 12.7B technical report
Junghwan Lim, Sungmin Lee, Dongseok Kim +22
We introduce Motif-2-12.7B, a new open-weight foundation model that pushes the efficiency frontier of large language models by combining architectural innovation with system-level…
Grouped Differential Attention
Junghwan Lim, Sungmin Lee, Dongseok Kim +7
The self-attention mechanism, while foundational to modern Transformer architectures, suffers from a critical inefficiency: it frequently allocates substantial attention to redunda…
G2L:From Giga-Scale to Cancer-Specific Large-Scale Pathology Foundation Models via Knowledge Distillation
Yesung Cho, Sungmin Lee, Geongyu Lee +3
Recent studies in pathology foundation models have shown that scaling training data, diversifying cancer types, and increasing model size consistently improve their performance. Ho…
Expanding Foundational Language Capabilities in Open-Source LLMs through a Korean Case Study
Junghwan Lim, Gangwon Jo, Sungmin Lee +16
We introduce Llama-3-Motif, a language model consisting of 102 billion parameters, specifically designed to enhance Korean capabilities while retaining strong performance in Englis…
Motif 2.6B Technical Report
Junghwan Lim, Sungmin Lee, Dongseok Kim +22
Recent advancements in Large Language Models (LLMs) have revolutionized artificial intelligence, yet developing an effective foundational LLM that balances high performance with co…
LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction
Sungmin Lee, Minju Kang, Joonhee Lee +6
Question-answering (QA) interfaces powered by large language models (LLMs) present a promising direction for improving interactivity with HVAC system insights, particularly for non…
Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation
Yujin Shin, Kichang Lee, Sungmin Lee +3
While federated learning leverages distributed client resources, it faces challenges due to heterogeneous client capabilities. This necessitates allocating models suited to clients…