7 papers
Token-Controlled Re-ranking for Sequential Recommendation via LLMs
Wenxi Dai, Wujiang Xu, Pinhuan Wang +1
The widespread adoption of Large Language Models (LLMs) as re-rankers is shifting recommender systems towards a user-centric paradigm. However, a significant gap remains: current r…
Graph4MM: Weaving Multimodal Learning with Structural Information
Xuying Ning, Dongqi Fu, Tianxin Wei +2
Real-world multimodal data usually exhibit complex structural relationships beyond traditional one-to-one mappings like image-caption pairs. Entities across modalities interact in…
OmniRouter: Budget and Performance Controllable Multi-LLM Routing
Kai Mei, Wujiang Xu, Minghao Guo +2
Large language models (LLMs) deliver superior performance but require substantial computational resources and operate with relatively low efficiency, while smaller models can effic…
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search
Zujie Liang, Feng Wei, Wujiang Xu +3
Recent advancements in large language models (LLMs) have shown remarkable potential in automating machine learning tasks. However, existing LLM-based agents often struggle with low…
iAgent: LLM Agent as a Shield between User and Recommender Systems
Wujiang Xu, Yunxiao Shi, Zujie Liang +6
Traditional recommender systems usually take the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms. However, th…
A-MEM: Agentic Memory for LLM Agents
Wujiang Xu, Zujie Liang, Kai Mei +3
While large language model (LLM) agents can effectively use external tools for complex real-world tasks, they require memory systems to leverage historical experiences. Current mem…