18 citations · 20 across the 22 of their papers we have counts for
27 papers
RIWANav: Recursive World-Action Models with Self-Improvement for Urban Navigation
Jing Xie, Shouwei Ruan, Yubin Wang +4
Long-horizon urban navigation requires sequential local decisions whose errors can compound over time. Imitation learning (IL) rarely learns from failures, while physical trial-and…
PreAct-Nav: Agentic Reasoning Before Action for Urban Navigation
Jing Xie, Shouwei Ruan, Yubin Wang +4
Urban navigation requires embodied agents to pursue long-horizon goals through local decisions based on egocentric observations. However, existing agentic navigation methods often…
UniNDM: A Unified Noise-driven Detection and Mitigation Framework Against Sexual Content in Text-to-Image Generation
Yao Huang, Yitong Sun, Huanran Chen +8
Despite the impressive generative capabilities of text-to-image diffusion models, they remain vulnerable to implicit sexual prompts, where subtle cues disguised as benign terms or…
AlloSpatial: Agentic Harness Framework for Spatial Reasoning in Foundation Models
Shouwei Ruan, Bin Wang, Zhenyu Wu +5
Multimodal Foundation Models (MFMs) have made substantial progress, yet remain fragile in spatial reasoning over the physical world. A key bottleneck lies in their inability to tra…
WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts
Yuxin Meng, Yuhan Suo, Junjie Wang +9
Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the requirement-induced states and transitions that determine whether a page…
Improving Safety Alignment via Balanced Direct Preference Optimization
Shiji Zhao, Mengyang Wang, Shukun Xiong +7
With the rapid development and widespread application of Large Language Models (LLMs), their potential safety risks have attracted widespread attention. Reinforcement Learning from…
Mind over Space: Can Multimodal Large Language Models Mentally Navigate?
Qihui Zhu, Shouwei Ruan, Xiao Yang +6
Despite the widespread adoption of MLLMs in embodied agents, their capabilities remain largely confined to reactive planning from immediate observations, consistently failing in sp…
World2Mind: Cognition Toolkit for Allocentric Spatial Reasoning in Foundation Models
Shouwei Ruan, Bin Wang, Zhenyu Wu +4
Achieving robust spatial reasoning remains a fundamental challenge for current Multimodal Foundation Models (MFMs). Existing methods either overfit statistical shortcuts via 3D gro…
NDM: A Noise-driven Detection and Mitigation Framework against Implicit Sexual Intentions in Text-to-Image Generation
Yitong Sun, Yao Huang, Ruochen Zhang +4
Despite the impressive generative capabilities of text-to-image (T2I) diffusion models, they remain vulnerable to generating inappropriate content, especially when confronted with…
Eva-VLA: Evaluating Vision-Language-Action Models' Robustness Under Real-World Physical Variations
Hanqing Liu, Shouwei Ruan, Jiahuan Long +6
Vision-Language-Action (VLA) models have emerged as promising solutions for robotic manipulation, yet their robustness to real-world physical variations remains critically underexp…
From reactive to cognitive: brain-inspired spatial intelligence for embodied agents
Shouwei Ruan, Liyuan Wang, Caixin Kang +4
Spatial cognition enables adaptive goal-directed behavior by constructing internal models of space. Robust biological systems consolidate spatial knowledge into three interconnecte…
Breaking the Ceiling: Exploring the Potential of Jailbreak Attacks through Expanding Strategy Space
Yao Huang, Yitong Sun, Shouwei Ruan +3
Large Language Models (LLMs), despite advanced general capabilities, still suffer from numerous safety risks, especially jailbreak attacks that bypass safety protocols. Understandi…
Mitigating Overthinking in Large Reasoning Models via Manifold Steering
Yao Huang, Huanran Chen, Shouwei Ruan +3
Recent advances in Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in solving complex tasks such as mathematics and coding. However, these models frequently…
MoAPT: Mixture of Adversarial Prompt Tuning for Vision-Language Models
Shiji Zhao, Qihui Zhu, Shukun Xiong +7
Large pre-trained Vision Language Models (VLMs) demonstrate excellent generalization capabilities but remain highly susceptible to adversarial examples, posing potential security r…
The Path to Reconciling Quality and Safety in Text-to-Image Generation: Dataset, Method, and Evaluation
Shouwei Ruan, Zhenyu Wu, Yao Huang +5
Content safety is a fundamental challenge for text-to-image (T2I) models, yet prevailing methods enforce a debilitating trade-off between safety and generation quality. We argue th…
When Lighting Deceives: Exposing Vision-Language Models' Illumination Vulnerability Through Illumination Transformation Attack
Hanqing Liu, Shouwei Ruan, Yao Huang +2
Vision-Language Models (VLMs) have achieved remarkable success in various tasks, yet their robustness to real-world illumination variations remains largely unexplored. To bridge th…
AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?
Shouwei Ruan, Hanqing Liu, Yao Huang +5
Vision Language Models (VLMs) have exhibited remarkable generalization capabilities, yet their robustness in dynamic real-world scenarios remains largely unexplored. To systematica…
OODFace: Benchmarking Robustness of Face Recognition under Common Corruptions and Appearance Variations
Caixin Kang, Yubo Chen, Shouwei Ruan +5
With the rise of deep learning, facial recognition technology has seen extensive research and rapid development. Although facial recognition is considered a mature technology, we f…
Real-world Adversarial Defense against Patch Attacks based on Diffusion Model
Xingxing Wei, Caixin Kang, Yinpeng Dong +4
Adversarial patches present significant challenges to the robustness of deep learning models, making the development of effective defenses become critical for real-world applicatio…
Exploring the Robustness of Decision-Level Through Adversarial Attacks on LLM-Based Embodied Models
Shuyuan Liu, Jiawei Chen, Shouwei Ruan +2
Embodied intelligence empowers agents with a profound sense of perception, enabling them to respond in a manner closely aligned with real-world situations. Large Language Models (L…
Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models
Shouwei Ruan, Yinpeng Dong, Hanqing Liu +3
Vision-Language Pre-training (VLP) models like CLIP have achieved remarkable success in computer vision and particularly demonstrated superior robustness to distribution shifts of…
Towards Transferable Targeted 3D Adversarial Attack in the Physical World
Yao Huang, Yinpeng Dong, Shouwei Ruan +3
Compared with transferable untargeted attacks, transferable targeted adversarial attacks could specify the misclassification categories of adversarial samples, posing a greater thr…
Improving Viewpoint Robustness for Visual Recognition via Adversarial Training
Shouwei Ruan, Yinpeng Dong, Hang Su +3
Viewpoint invariance remains challenging for visual recognition in the 3D world, as altering the viewing directions can significantly impact predictions for the same object. While…
Towards Viewpoint-Invariant Visual Recognition via Adversarial Training
Shouwei Ruan, Yinpeng Dong, Hang Su +3
Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object. Alt…