3 papers
cs.AI2026
CostNav: A Navigation Benchmark for Real-World Economic-Cost Evaluation of Physical AI Agents
Haebin Seong, Sungmin Kim, Yongjun Cho +20
Current navigation benchmarks focus on task success but do not capture the economic constraints essential for commercializing autonomous delivery systems. We introduce CostNav, an…
cs.AI2026
D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI
Suhwan Choi, Jaeyoon Jung, Haebin Seong +7
Large language models leverage internet-scale text data, yet embodied AI remains constrained by the prohibitive costs of physical trajectory collection. Desktop environments -- par…
cs.CV2025
KOFFVQA: An Objectively Evaluated Free-form VQA Benchmark for Large Vision-Language Models in the Korean Language
Yoonshik Kim, Jaeyoon Jung
The recent emergence of Large Vision-Language Models(VLMs) has resulted in a variety of different benchmarks for evaluating such models. Despite this, we observe that most existing…