1 citations · 1 across the 1 of their papers we have counts for
5 papers
Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics
Open-H-Embodiment Consortium, :, Nigel Nelson +213
Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medic…
AutoMaAS: Self-Evolving Multi-Agent Architecture Search for Large Language Models
Bo Ma, Hang Li, ZeHua Hu +3
Multi-agent systems powered by large language models have demonstrated remarkable capabilities across diverse domains, yet existing automated design approaches seek monolithic solu…
AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems
Bo Ma, Hang Li, ZeHua Hu +3
Foundation models have revolutionized artificial intelligence, yet their application in recommender systems remains limited by reasoning opacity and knowledge constraints. This pap…
LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing
Bo Ma, Hang Li, ZeHua Hu +3
Contemporary generative recommendation systems face significant challenges in handling multimodal data, eliminating algorithmic biases, and providing transparent decision-making pr…
AgentRec: Next-Generation LLM-Powered Multi-Agent Collaborative Recommendation with Adaptive Intelligence
Bo Ma, Hang Li, ZeHua Hu +3
Interactive conversational recommender systems have gained significant attention for their ability to capture user preferences through natural language interactions. However, exist…