Erasing individual identities from Vision-Language Models (VLMs) is uniquely challenging because personal data is entangled across modalities rather than stored as isolated attributes. However, existing multimodal unlearning benchmarks primarily evaluate attribute-centric forgetting, overlooking the more critical objec...
Xiong-Tao Sun, Hui Li, Tian-Tong Wu et al.· 0 citations
Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous agents, whose semantic states are dynamically refined through a recurrent process known as collaborative reflection. While this mechanism improves recommendation quality, it si...
Yu-Rong Hao, Wen Zhou, Guo-Wei Guan et al.· 0 citations
This work introduces SynChain, a self-synthesized attack paradigm utilizing persistence-aware directed supervised fine-tuning to induce agents to create poisoned yet benign-looking artifacts, proving that securing CUAs requires provenance-aware reasoning over cross-task execution trajectories.
Fuyao Zhang, Jiaming Zhang, Che Wang et al.· 0 citations
This survey examines the protective paradigm that has grown around this intervention point, and finds that most protections are still validated mainly against static or weakly adaptive adversaries, while evidence beyond controlled benchmarks remains scarce.
Jiaming Zhang, Bo-Yang Chen, Zhe-Rui Li et al.· 0 citations
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