15 papers
COMPASS: Cognitive MCTS-Guided Process Alignment for Safe Search Agents
Wenkai Shen, Pengyang Zhou, Jiahe Xu +5
LLM-powered search agents enable multi-step reasoning and tool use. However, these capabilities introduce retrieval-induced safety degradation, as harmful intents may decompose int…
An item is worth one token in Multimodal Large Language Models-based Sequential Recommendation
Qiyong Zhong, Jiajie Su, Ming Yang +3
Sequential recommendations (SR) predict users' future interactions based on their historical behavior. The rise of Large Language Models (LLMs) has brought powerful generative and…
Generalizable Multimodal Large Language Model Editing via Invariant Trajectory Learning
Jiajie Su, Haoyuan Wang, Xiaohua Feng +6
Knowledge editing emerges as a crucial technique for efficiently correcting incorrect or outdated knowledge in large language models (LLM). Existing editing methods rely on a rigid…
Potent but Stealthy: Rethink Profile Pollution against Sequential Recommendation via Bi-level Constrained Reinforcement Paradigm
Jiajie Su, Zihan Nan, Yunshan Ma +6
Sequential Recommenders, which exploit dynamic user intents through interaction sequences, is vulnerable to adversarial attacks. While existing attacks primarily rely on data poiso…
FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models
Xinting Liao, Weiming Liu, Jiaming Qian +6
Federated prompt learning (FPL) for vision-language models is a powerful approach to collaboratively adapt models across distributed clients while preserving data privacy. However,…
Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation
Jiajie Su, Qiyong Zhong, Yunshan Ma +5
Session-based recommendation (SBR) predicts the next item based on anonymous sessions. Traditional SBR explores user intents based on ID collaborations or auxiliary content. To fur…