Papers by Xiaotong Ye
AlignUSER: Human-Aligned LLM Agents via World Models for Recommender System Evaluation (2026.acl-long)
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| Challenge: | Existing evaluation practices for recommender systems rely on few-shot prompting and offline metrics are often misaligned with online behavior. |
| Approach: | They propose a framework that learns world-model-driven agents from human interactions. |
| Outcome: | The proposed framework enables agents to express rich preferences and feedback in natural language and interact with recommender systems in a simulation. |
MobileCity: An Efficient Framework for Large-Scale Urban Behavior Simulation (2026.eacl-industry)
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| Challenge: | Existing methods for simulating realistic urban behaviors rely on static profiles and synchronous inference pipelines that hinder scalability. |
| Approach: | They propose a lightweight generative agent framework for city-scale simulation powered by cognitively-grounded generative agents. |
| Outcome: | Experiments with 4,000 agents show that MobileCity generates more human-like urban dynamics than baselines while maintaining high computational efficiency. |