activity
20242026
collaborators

7 papers

cs.RO2026

HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution

Kyochul Jang, Seohyeon Park, Ohchul Kwon +9

As robotic hardware and learning methods advance, humanoids need tools to perform tasks beyond their inherent physical limits. Successful tool use requires selecting a suitable too…

cs.AI2026

SafeBranch: Branch-Pair Safety Alignment for Embodied Agents

Hyunse Lee, Jiwoo Jeong, Haneul Lee +3

Vision-language-model-based embodied agents can complete instructed tasks but often violate safety constraints in the process, a problem recently framed as interactive safety. Trai…

cs.CL2026

K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts

Nahyun Lee, Dongkeun Yoon, Guijin Son +12

Frontier model evaluations are shifting from foundational capabilities (e.g., instruction following and reasoning) toward compositional, agentic ones, but Korean agentic benchmarks…

cs.HC2025

InFerActive: Interactive Tree-Based Exploration of LLM Sampling for Safety Evaluation

Junhyeong Hwangbo, Soohyun Lee, Hyeon Jeon +4

Even LLMs that appear safe during evaluation can still produce harmful responses in deployment. Because stochastic sampling yields different responses to the same prompt, low-proba…

cs.CL2025

DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues

Kyochul Jang, Donghyeon Lee, Kyusik Kim +4

Existing function-calling benchmarks focus on single-turn interactions. However, they overlook the complexity of real-world scenarios. To quantify how existing benchmarks address p…

cs.CL2024

ETHIC: Evaluating Large Language Models on Long-Context Tasks with High Information Coverage

Taewhoo Lee, Chanwoong Yoon, Kyochul Jang +4

Recent advancements in large language models (LLM) capable of processing extremely long texts highlight the need for a dedicated evaluation benchmark to assess their long-context c…

cs.DB2024

KU-DMIS at EHRSQL 2024:Generating SQL query via question templatization in EHR

Hajung Kim, Chanhwi Kim, Hoonick Lee +5

Transforming natural language questions into SQL queries is crucial for precise data retrieval from electronic health record (EHR) databases. A significant challenge in this proces…