9 papers
Towards Compressive and Scalable Recurrent Memory
Yunchong Song, Jushi Kai, Liming Lu +2
Transformers face a quadratic bottleneck in attention when scaling to long contexts. Recent approaches introduce recurrent memory to extend context beyond the current window, yet t…
DREAM: Dynamic Red-teaming across Environments for AI Models
Liming Lu, Xiang Gu, Junyu Huang +5
Large Language Models (LLMs) are increasingly used in agentic systems, where their interactions with diverse tools and environments create complex, multi-stage safety challenges. H…
EAT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Models
Liming Lu, Xiang Gu, Shuchao Pang +5
Research endeavors have been made in learning robust Multimodal Large Language Models (MLLMs) against jailbreak attacks. However, existing methods for improving MLLMs' robustness s…
Multimodal Robust Prompt Distillation for 3D Point Cloud Models
Xiang Gu, Liming Lu, Xu Zheng +3
Adversarial attacks pose a significant threat to learning-based 3D point cloud models, critically undermining their reliability in security-sensitive applications. Existing defense…
RoboView-Bias: Benchmarking Visual Bias in Embodied Agents for Robotic Manipulation
Enguang Liu, Siyuan Liang, Liming Lu +4
The safety and reliability of embodied agents rely on accurate and unbiased visual perception. However, existing benchmarks mainly emphasize generalization and robustness under per…
FERD: Fairness-Enhanced Data-Free Robustness Distillation
Zhengxiao Li, Liming Lu, Xu Zheng +4
Data-Free Robustness Distillation (DFRD) aims to transfer the robustness from the teacher to the student without accessing the training data. While existing methods focus on overal…