Generative agents offer promising capabilities for simulating realistic urban behaviors. However, existing methods often rely on static profiles, oversimplified behavioral logic, and synchronous inference pipelines that hinder scalability. We present MobileCity, a lightweight generative-agent framework for city-scale simulation powered by cognitively-grounded generative agents. Each agent acts based on its needs, habits, and obligations, evolving over time. Agents are initialized from survey-based demographic data and navigate a realistic multimodal transportation network spanning multiple types of vehicles. To achieve scalability, we introduce asynchronous batched LLM inference during action selection and a low-token communication mechanism. Experiments with 4,000 agents demonstrate that MobileCity generates more human-like urban dynamics than baselines while maintaining high computational efficiency. Our code is publicly available at https://github.com/Tony-Yip/MobileCity.

Evaluating recommender systems remains challenging due to the gap between offline metrics and real user behavior, as well as the scarcity of interaction data. Recent work explores large language model (LLM) agents as synthetic users, yet they typically rely on few-shot prompting, which yields a shallow understanding of the environment and limits their ability to faithfully reproduce user actions. We introduce AlignUSER, a framework that learns world-model-driven agents from human interactions. Given rollout sequences of actions and states, we formalize world modeling as a next state prediction task that helps the agent internalize the environment. To align actions with human personas, we generate counterfactual trajectories around demonstrations and prompt the LLM to compare its decisions with human choices, identify suboptimal actions, and extract lessons. The learned policy is then used to drive agent interactions with the recommender system. We evaluate AlignUSER across multiple datasets and demonstrate closer alignment with genuine humans than prior work, both at the micro and macro levels.