4 citations · 5 across the 8 of their papers we have counts for
4 papers · 2 filters
RealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce
Xinwei Yang, Kelong Mao, Yudong Guo +4
Large language models are reshaping ecommerce from static recommenders into interactive shopping assistants, yet real-world shopping requires session-level decision support: users…
Learning from Online User Feedback for Shopping Agents
Haobo Zhang, Kelong Mao, Sulong Xu +2
Large language model-based shopping agents are increasingly deployed in real-world e-commerce platforms, generating massive amounts of user interaction logs that provide valuable s…
ComboShoppingBench: Evaluating LLM Agents for Budget-Constrained Basket Shopping with Coupons
Adrian Li, Kelong Mao, Yudong Guo +7
Real-world shopping often requires constructing a basket of complementary items rather than retrieving a single product. Such combo-shopping tasks arise in device setup, meal prepa…
SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use
Jiayin Zhu, Kelong Mao, Yudong Guo +4
Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines. In realistic skill repositories, ove…