The results provide significant, multi-perspective evidence that social effects of data could be a causal driver of LLM capability decay in continual pre-training, thereby motivating routine"cognitive health checks" for deployed and evolving LLMs.
Shuo Xing, Junyuan Hong, Yi-Fan Wang et al.· arXiv.org· 3 citations
While Large Language Models (LLMs) have demonstrated striking capabilities on frontier mathematical problems, it remains unclear whether they possess the structural mathematical understanding underlying their solutions. In this paper, we take a first step toward systematically studying mathematical understanding in LLM...
Shuo Xing, Zi-Lin Dai, Cheng-Yuan Qian et al.· 0 citations
CinematicVQA is introduced, the first-of-its-kind benchmark for cinematic video understanding that goes beyond technique recognition to evaluate film-grammar reasoning, utilizing the introduced Cinematic Scene Graph (CSG), a structured representation that links filming techniques to their perceptual effects and narrati...
Shuo Xing, Pooja Verlani, Balu Adsumilli et al.· 0 citations
This survey delivers a comprehensive and critical synthesis of the emerging role of GenAI across the autonomous driving stack, delving into the frontier applications of GenAI in image, LiDAR, trajectory, occupancy, and video generation, as well as LLM-guided reasoning and decision-making.
Yu-Ping Wang, Shuo Xing, C. Cui et al.· ACM Computing Surveys· 60 citations· ⚡2
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