5 papers
Experience-Sensitive Game Learning: A Behavioral Study of Humans and Language Agents
Yingying Guo, Zhuoxuan Ju, Ruibo Ming +2
Large language model agents are increasingly evaluated through games, but most benchmarks emphasize final outcomes rather than how players learn from repeated interaction. We study…
A Style-Based Profiling Framework for Quantifying the Synthetic-to-Real Gap in Autonomous Driving Datasets
Dingyi Yao, Xinyao Han, Ruibo Ming +5
Ensuring the reliability of autonomous driving perception systems requires extensive environment-based testing, yet real-world execution is often impractical. Synthetic datasets ha…
Synthetic Dataset Evaluation Based on Generalized Cross Validation
Zhihang Song, Dingyi Yao, Ruibo Ming +3
With the rapid advancement of synthetic dataset generation techniques, evaluating the quality of synthetic data has become a critical research focus. Robust evaluation not only dri…
A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches
Ruibo Ming, Zhewei Huang, Jingwei Wu +5
Future Frame Synthesis (FFS), the task of generating subsequent video frames from context, represents a core challenge in machine intelligence and a cornerstone for developing pred…
ARCON: Advancing Auto-Regressive Continuation for Driving Videos
Ruibo Ming, Jingwei Wu, Zhewei Huang +4
Recent advancements in auto-regressive large language models (LLMs) have led to their application in video generation. This paper explores the use of Large Vision Models (LVMs) for…