Diffusion feature, i.e., intermediate activations extracted from diffusion model backbones, has emerged as a promising approach for unifying generation and vision understanding (discrimination). Despite the advancement of diffusion backbones into Diffusion Transformers (DiT), it has been found in previous literature th...
Ben-Yuan Meng, Yi-Liang Zhang, Jin-Wen Wu et al.· 0 citations
Explicit reasoning in large language models (LLMs) is often associated with reliability, yet it can still pose risks when a model infers harmful goals under a benign context. We identify a distinct challenge: ensuring the safety of the \emph{multi-step reasoning process} itself, where locally coherent steps may still l...
Hao Yang, Ke Ma, Yangbangyan Jiang et al.· 0 citations
The visual anchoring effect is revealed: by enforcing consistency, the mechanism prevents the generated content from drifting away from the original harmful intent, thereby eliminating the model's natural safety escape route from harmful to benign content.
Peng Li, Qian-Qian Xu, Yangbangyan Jiang et al.· 0 citations
The CVPR 2026@AdvML Workshop Challenge on adversarial multimodal attacks against autonomous-driving VLAs is presented, providing a practical reference for future robustness evaluation and defense design in multimodal autonomous-driving systems.
Tian-Yuan Zhang, Zonglei Jing, Jiangfan Liu et al.· arXiv.org· 0 citations
ZenGen, an integrated framework for measuring, internalizing, and grounding social intelligence, and Actio, a harness-controlled inference architecture that routes four typed supports into reasoning demonstrate the effectiveness of typed runtime support.
ZenGen Team, Xiang Ao, Jingping Bi et al.· 0 citations
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