Author

Kai-Jie Zhu

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Preprint Aug 2026

Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed

First, it is demonstrated that quantization is significantly more effective in preserving trustworthiness compared to pruning, and more importantly, it is demonstrated that compressing a reliable large model via quantization can produce SLMs with superior trustworthiness and adaptability compared to using small models...

Hao-Kun Lin, Kai-Jie Zhu, Hao-Bo Xu et al. · 2 citations

#artificial intelligence Preprint Sep 2026

Can LLMs Discover Scientific Laws in Real and Parallel Worlds?

It is found that predictive fit can diverge from scientific validity, memorization shapes whether models reproduce or move beyond published formulas, and the best-of-N study reveals a selection bottleneck.

Yi-Ming Huang, Zi-Chen Liu, Junxia Cui et al. · 2 citations