5 papers
PromptPath: Prompt-Adaptive Computational Pathways for In-Context Learning
Hangrui Zhang, Feifei Shao, Yawei Luo +6
In-context learning (ICL) has attracted increasing attention for enabling models to perform new tasks using only a few ``input--output'' prompt examples. However, existing approach…
Closed-Loop Bidirectional Prompting for Adversarial Robustness of Vision Language Models
Xiao Liu, Jiaxiang Liu, Boci Peng +6
Vision Language Models adapt well to downstream tasks but are highly vulnerable to adversarial perturbations that disrupt cross-modal semantic alignment. Existing defenses are larg…
MindAdapter: Few-Shot Parameter-Efficient Residual Calibration of Cross-Subject Brain-to-Visual Decoding Models
Jiaxiang Liu, Jiawei Du, Xupeng Chen +4
Cross-subject brain-to-visual decoding remains a core challenge in brain-computer interfaces due to severe inter-individual variability that induces systematic subject-specific fun…
Med-R2: An Adversarial Benchmark for Evidence-Grounded Reasoning in Medical VLMs
Wen Ma, Fucheng Niu, Zhiting Fan +3
Vision-language models have demonstrated impressive capabilities in general medical visual question answering, yet due to limited interpretability, it remains unclear whether their…
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…