5 papers
Band Together: Untargeted Adversarial Training with Multimodal Coordination against Evasion-based Promotion Attacks
Guanmeng Xian, Ning Yang, Philip S. Yu
Multimodal recommender systems exploit visual and textual signals to alleviate data sparsity, but this also makes them more vulnerable to evasion-based promotion attacks. Existing…
Manifold-Constrained Adversarial Training for Long-Tailed Robustness via Geometric Alignment
Guanmeng Xian, Ning Yang, Philip S. Yu
Adversarial training is effective on balanced datasets, but its robustness degrades under longtailed class distributions, where tail classes suffer high robust error and unstable d…
AdvEvo-MARL: Shaping Internalized Safety through Adversarial Co-Evolution in Multi-Agent Reinforcement Learning
Zhenyu Pan, Yiting Zhang, Zhuo Liu +13
LLM-based multi-agent systems excel at planning, tool use, and role coordination, but their openness and interaction complexity also expose them to jailbreak, prompt-injection, and…
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety
Zhenyu Pan, Yiting Zhang, Yutong Zhang +8
Multi-agent systems (MAS) built on multimodal large language models exhibit strong collaboration and performance. However, their growing openness and interaction complexity pose se…
FairReason: Balancing Reasoning and Social Bias in MLLMs
Zhenyu Pan, Yutong Zhang, Jianshu Zhang +6
Multimodal Large Language Models (MLLMs) already achieve state-of-the-art results across a wide range of tasks and modalities. To push their reasoning ability further, recent studi…