Author

Hao-Kun Lin

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Preprint Oct 2026

FisherEdit: Fisher Information Guided Knowledge Editing for Large Language Models

FisherEdit is proposed, a theoretically grounded method built on Fisher geometry, which replaces coarse activation-space projections with importance-aware regularization in parameter space and achieves state-of-the-art performance while better preserving model stability and downstream task accuracy.

Yi-Zhou Yang, Jia-Hong Fu, Zhi-Wu Wang et al. · 0 citations

#artificial intelligence Preprint Sep 2026

STEPQuant: When and Where Errors Matter in Delta-Rule Recurrent State Quantization

STEPQuant is proposed, a spatial-temporal post-training quantization framework for Delta-rule recurrent states that allocates precision according to error magnitude and memory lifetime, and jointly fits key-row and value-column scales based on state distributions and key-row impact on output error.

Bing-Chen Yao, Hao-Bo Xu, Hao-Kun Lin et al. · 0 citations

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

Jul 2026

MAC-Splat: Multi-Attribute Consistency for High-Fidelity Sparse-View Reconstruction

Results indicate that a direct, multi-attribute 3D consistency objective, when combined with high-quality correspondences, is effective for addressing the ill-posed sparse-view reconstruction problem.

Jinqian Yang, Yichen Wu, Wanhua Li et al. · arXiv.org · 1 citation