reviews: 
Comments: This paper introduces **CityBench**, an innovative platform designed to evaluate the capabilities of large language models (LLMs) and visual language models (VLMs) in solving diverse urban tasks. CityBench integrates three core components: **CityData**, which collects and processes geospatial, visual, and human activity data; **CitySim**, a simulator that models urban dynamics like individual mobility, visual environments, and microscopic traffic behavior; and **CityBench**, which offers a comprehensive evaluation of eight urban tasks in two categories — perception & understanding and planning & decision-making.

The authors conducted extensive experiments involving 30 LLMs and VLMs across 13 global cities, evaluating tasks such as geospatial prediction, image geolocalization, traffic signal control, and mobility prediction. The results highlight the potential of LLMs and VLMs in handling urban tasks that require commonsense reasoning and semantic understanding, while also exposing challenges in tasks requiring precise professional knowledge and high-level reasoning, such as geospatial prediction and traffic control.

The paper contributes significantly by providing a scalable and systematic evaluation framework, uncovering strengths and limitations of current LLMs/VLMs in urban settings, and paving the way for further development and application of these models in real-world urban scenarios.
Strengths: 1. **High Level of Innovation**:
    - The paper introduces **CityBench**, a highly innovative and comprehensive evaluation platform. CityBench stands out by integrating multi-modal data and interactive simulations to systematically evaluate the performance of LLMs and VLMs in urban tasks, filling a significant gap in the evaluation benchmarks for urban research.
    - The combination of **CityData** and **CitySim** offers researchers a robust tool that simulates fine-grained dynamics across 13 cities. This large-scale, multi-city, multi-modal simulation environment sets CityBench apart from previous work, which has not addressed this level of complexity and scale.

2. **Comprehensive and Detailed Experiment Design**:
    - The experiment design is meticulous, covering both perception tasks (such as image geolocalization and infrastructure inference) and decision-making tasks (such as traffic signal control and mobility prediction). This creates a thorough evaluation framework that includes both straightforward perception tasks and more complex decision-making scenarios.
    - The authors conducted extensive experiments with 30 different LLMs and VLMs, providing a rich dataset and thorough result analysis, validating CityBench as an effective benchmark for urban research.
    - Additionally, the **error analysis and geospatial bias analysis** demonstrate a deep understanding of model behavior across different environments, offering valuable insights for future improvements.

3. **Practicality and Forward-looking Approach**:
    - The paper goes beyond theory by offering an open-source evaluation platform with easy-to-use APIs, significantly increasing its usability and scalability for further urban research applications.
    - It explores the potential of LLMs in urban research, particularly in dynamic and interactive environments, showcasing the future applications of LLMs in urban planning, traffic management, and other real-world urban scenarios.
Weaknesses: 1. **Limited Task Diversity**:
    - While the paper has designed a rich set of urban tasks, there is room for further expansion to include tasks that cover broader social, economic, and environmental aspects of urban research. Future iterations could consider incorporating more tasks related to community planning or disaster response, which would enhance CityBench’s applicability across a wider range of urban research domains.
Recommendation: 10
Reviewer confidence: 4
Soundness/correctness: 4
Clarity: 4