16 citations · 38 across the 28 of their papers we have counts for
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cs.CV2026
Seeing Right but Saying Wrong: Inter- and Intra-Layer Refinement in MLLMs without Training
Shezheng Song, Shasha Li, Jie Yu
Multimodal Large Language Models (MLLMs) have demonstrated strong capabilities across a variety of vision-language tasks. However, their internal reasoning often exhibits a critica…
cs.CR2026
JPU: Bridging Jailbreak Defense and Unlearning via On-Policy Path Rectification
Xi Wang, Songlei Jian, Shasha Li +5
Despite extensive safety alignment, Large Language Models (LLMs) often fail against jailbreak attacks. While machine unlearning has emerged as a promising defense by erasing specif…
cs.CV2026
Where Does Vision Meet Language? Understanding and Refining Visual Fusion in MLLMs via Contrastive Attention
Shezheng Song, Shasha Li, Shan Zhao +6
Multimodal Large Language Models (MLLMs) have achieved remarkable progress in vision-language understanding, yet how they internally integrate visual and textual information remain…