collaborators

7 papers

cs.AI2026

Who's Behind the Text? Robust Model Authorship Attribution

Minseok Ko, Hyunseok Lee, Woomin Song +3

As large language models (LLMs) are increasingly used to generate text, their misuse has become a growing societal problem, with implications for academic misconduct, model distill…

cs.RO2026

PointWAM: 3D World Action Modeling for Dexterous Robotic Manipulation

Chunghyun Park, Beomjun Kim, Seungcheol Park +5

World action models jointly learn to forecast world dynamics and predict robot actions, such that the learned internal world dynamics guide accurate actions. Existing approaches ty…

cs.RO2026

Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations

Beomjun Kim, Seong Hyeon Park, Seunghoon Sim +3

Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real ro…

cs.AI2026

ExComm: Exploration-Stage Communication for Error-Resilient Agentic Test-Time Scaling

Woomin Song, Beomjun Kim, Daewon Choi +4

A common failure mode in long-horizon agentic test-time scaling is error propagation, where factual errors or invalid deductions introduced at intermediate steps persist in the age…

cs.RO2026

RLDX-1 Technical Report

Dongyoung Kim, Huiwon Jang, Myungkyu Koo +65

While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene underst…

cs.LG2025

ReGUIDE: Data Efficient GUI Grounding via Spatial Reasoning and Search

Hyunseok Lee, Jeonghoon Kim, Beomjun Kim +7

Recent advances in Multimodal Large Language Models (MLLMs) have enabled autonomous agents to interact with computers via Graphical User Interfaces (GUIs), where accurately localiz…

cs.LG2025

Peri-LN: Revisiting Normalization Layer in the Transformer Architecture

Jeonghoon Kim, Byeongchan Lee, Cheonbok Park +7

Selecting a layer normalization (LN) strategy that stabilizes training and speeds convergence in Transformers remains difficult, even for today's large language models (LLM). We pr…