Author

Jing Wang

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#artificial intelligence Preprint Sep 2026

TReVS: Integrating Textual Relevance and Visual Saliency for Efficient Vision-Language Model Token Pruning

TReVS is proposed, a training-free framework that combines textual relevance with vision-encoder saliency for pre-LLM pruning and leverages high-variance attention heads to remove task-irrelevant tokens at shallow-to-intermediate layers of the LLM.

Jing Wang, Zhi-Ping Wu, Dong-Dong Ren et al. · 0 citations

Preprint Jun 2026

Yuvion LLM: An Adversarially-Aware Large Language Model for Content And AI Safety

As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We argue that the essence of safety is adversarial: many failures arise not from natural inputs alone, but from strategic attempts to evade model policies and safeguards. How...

Ting Ma, Xiufeng Huang, Benlei Cui et al. · 0 citations