# CityPlanner: A Sandbox Agent for Executable Urban Planning

arXiv: https://arxiv.org/abs/2609.09578 · HTML: https://mlsanity.com/paper/2609.09578/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 9 Sep 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 13 of 13)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
- [ReTool: Reinforcement Learning for Strategic Tool Use in LLMs](https://mlsanity.com/paper/2504.11536/) (2025)
- [ToolRL: Reward is All Tool Learning Needs](https://mlsanity.com/paper/2504.13958/) (2025)
- [Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model](https://mlsanity.com/paper/2401.02051/) (2024)
- [Encouraging Good Processes Without the Need for Good Answers: Reinforcement Learning for LLM Agent Planning](https://mlsanity.com/paper/2508.19598/) (2025)
- [Can LLMs Help You at Work? A Sandbox for Evaluating LLM Agents in Enterprise Environments](https://mlsanity.com/paper/2510.27287/) (2025)
- [UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models](https://mlsanity.com/paper/2406.12360/) (2024)
- [Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation](https://mlsanity.com/paper/2602.11224/) (2026)
- [Process-Supervised Reinforcement Learning for Interactive Multimodal Tool-Use Agents](https://mlsanity.com/paper/2509.14480/) (2025)
- [Large language model empowered participatory urban planning](https://mlsanity.com/paper/2402.01698/) (2024)

## Abstract (arXiv)

Urban planning is a real-world spatial optimization problem that requires selecting feasible actions from large candidate spaces under practical objectives such as cost and service quality. Existing optimization and reinforcement learning methods are effective for fixed formulations, but often depend on task-specific representations and constraint handling. We propose CityPlanner, a sandbox-agent framework for executable urban planning. CityPlanner introduces UrbanSandbox, a unified file-based environment where agents inspect task files, generate plans, run evaluators, and revise decisions based on executable feedback. To make learning tractable, we further propose atomic-task reinforcement learning, which decomposes long sandbox trajectories into BuildPlan for initial construction and ImprovePlan for feedback-based refinement. Experiments on a real-world benchmark show that CityPlanner consistently outperforms heuristic, task-specific RL, and general LLM-agent baselines. Ablations verify the contributions of UrbanSandbox, atomic-task RL, and iterative deployment. We release the code and dataset at https://anonymous.4open.science/r/co-agent-C1C8