Author

Ke-Long Mao

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#artificial intelligence Preprint Sep 2026

RealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce

Large language models are reshaping ecommerce from static recommenders into interactive shopping assistants, yet real-world shopping requires session-level decision support: users reveal and revise constraints, coordinate multiple goals, and expect product-grounded recommendations over a full conversation. Existing ben...

Xin-Wei Yang, Ke-Long Mao, Yu-Dong Guo et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Coding Agent Memory Post-training: Unlocking the Memory Potential of Pre-trained File Operations for Long-Horizon Tasks via Reinforcement Learning

Language-model agents increasingly tackle long-horizon tasks whose interaction histories exceed the model's active context. Recent work has begun to use reinforcement learning to make memory control part of the policy, often relying on predefined memory tools within domain-specific training environments of relatively s...

Li-Rui Luo, Ke-Long Mao, He-Ming Xia et al. · 0 citations

Preprint Aug 2026

Learning from Online User Feedback for Shopping Agents

LOFA combines reinforcement learning over verifiable purchase outcomes with feedback-aware on-policy distillation, which identifies users' in-dialogue directives and converts them into dense token-level supervision, which captures both collaborative behavioral patterns and user-specific preferences.

Haobo Zhang, Kelong Mao, Sulong Xu et al. · 0 citations