23 citations · 23 across the 5 of their papers we have counts for
6 papers
Multimodal Safety Evaluation Should Measure Controllability Beyond Classification
Junhyeong Park, Hanwool Lee, DongGeon Lee +4
VLM safety is commonly evaluated through input- and output-level classification. Such classification is necessary, but it does not reveal whether a safety state is accessible or co…
EgoSafetyBench: A Diagnostic Egocentric Video Benchmark for Evaluating Embodied VLMs as Runtime Safety Guards
Siddhant Panpatil, Arth Singh, Mijin Koo +3
Vision-language models (VLMs) are increasingly proposed as runtime safety guards for embodied agents in homes and factories. A deployable guard must catch genuinely unsafe situatio…
PHASOR: Phase-Anchored Universal Action Representations for Humanoid Embodiments
Kihyun Kim, Chaeyun Kim, Jongho Shin +4
Learning a good action embedding space is fundamental to scalable robot policy learning, yet existing methods treat action latents as task-specific intermediates rather than first-…
XL-SafetyBench: A Country-Grounded Cross-Cultural Benchmark for LLM Safety and Cultural Sensitivity
Dasol Choi, Eugenia Kim, Jaewon Noh +14
Current LLM safety benchmarks are predominantly English-centric and often rely on translation, failing to capture country-specific harms. Moreover, they rarely evaluate a model's a…
COMPASS: A Framework for Evaluating Organization-Specific Policy Alignment in LLMs
Dasol Choi, DongGeon Lee, Brigitta Jesica Kartono +6
As large language models are deployed in high-stakes enterprise applications, from healthcare to finance, ensuring adherence to organization-specific policies has become essential.…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…