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#small language model Preprint Sep 2026

Backdoor as Probe: Test-Time Adversarial Defense for CLIP

Backdoor as Probe is proposed, a test-time adversarial defense for CLIP that improves average robust accuracy from 1.0\% to 52.3\% while retaining clean accuracy, achieving performance comparable to state-of-the-art methods with up to a \(5.7\times\) inference speedup.

Zhong-Qi Wang, Jie Zhang, Sen Nie et al. · 0 citations
#machine learning Preprint Sep 2026

One Attack to Fool Them All: Highly Transferable Black-Box Adversarial Attacks on Frontier MLLMs

Adversarial attacks have long posed a fundamental threat to machine learning systems. As multimodal large language models (MLLMs) rapidly evolve and become widely deployed, assessing their vulnerability to such attacks is essential for their safe use. In this work, we investigate whether a single adversarial image can...

Sen Nie, Jie Zhang, Zhong Ling Wang et al. · 0 citations
Preprint Sep 2026

Video-HolmesV2: Can MLLMs Reason with Spatio-Temporal Audio-Visual Evidence in Long Videos?

Multimodal Large Language Models have demonstrated impressive video understanding, yet their ability to reason over long-form narratives is often masked by visual-centric evaluations and inefficient context processing. Existing benchmarks over-rely on visual heuristics while marginalizing auditory cues, effectively red...

Zhao-Yang Wei, Zipeng Wang, Yu-She Cao et al. · 0 citations

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