Author

Xun Zhu

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#artificial intelligence Preprint Aug 2026

FitAQA: A Benchmark of Fitness Action Quality Assessment for Multimodal Large Language Models

FitAQA is introduced, a systematic benchmark for evaluating MLLMs in fitness AQA, containing 2,219 videos and 5,512 QA instances across 30 bodyweight exercises and a unified form error taxonomy is developed that defines 38 recurring form errors within six complementary quality dimensions.

Kai-Li Zheng, Kai-Wen Wang, Xun Zhu et al. · 1 citation

#artificial intelligence Preprint Sep 2026

Task-Level Natural Language Priors as Learning Signals for Low-Resource LLM Training

Large language models (LLMs) often struggle when low-resource training data are ambiguous or incomplete. Task-level natural-language priors can provide useful guidance in such settings, but existing approaches usually treat these priors as input context rather than as learning signals during training. We study how task...

JiangHua Gao, Xiao Zhang, Xun Zhu et al. · 0 citations