5 papers
FinSafetyBench: Evaluating LLM Safety in Real-World Financial Scenarios
Yutao Hou, Yihan Jiang, Yuhan Xie +5
Large language models (LLMs) are increasingly applied in financial scenarios. However, they may produce harmful outputs, including facilitating illegal activities or unethical beha…
STRONG-VLA: Decoupled Robustness Learning for Vision-Language-Action Models under Multimodal Perturbations
Yuhan Xie, Yuping Yan, Yunqi Zhao +2
Despite their strong performance in embodied tasks, recent Vision-Language-Action (VLA) models remain highly fragile under multimodal perturbations, where visual corruption and lin…
OutSafe-Bench: A Benchmark for Multimodal Offensive Content Detection in Large Language Models
Yuping Yan, Yuhan Xie, Yuanshuai Li +3
Since Multimodal Large Language Models (MLLMs) are increasingly being integrated into everyday tools and intelligent agents, growing concerns have arisen regarding their possible o…
BD-Merging: Bias-Aware Dynamic Model Merging with Evidence-Guided Contrastive Learning
Yuhan Xie, Chen Lyu
Model Merging (MM) has emerged as a scalable paradigm for multi-task learning (MTL), enabling multiple task-specific models to be integrated without revisiting the original trainin…
When Alignment Fails: Multimodal Adversarial Attacks on Vision-Language-Action Models
Yuping Yan, Yuhan Xie, Yixin Zhang +3
Vision-Language-Action models (VLAs) have recently demonstrated remarkable progress in embodied environments, enabling robots to perceive, reason, and act through unified multimoda…