# LiPUP-MA: A Residential Experience-centric Multi-Agent Framework for Living-in-the-loop Participatory Urban Planning

arXiv: https://arxiv.org/abs/2412.20505 · HTML: https://mlsanity.com/paper/2412.20505/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Placed in

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

## Builds on (most cited first, 6 of 6)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024)
- [Humanoid Agents: Platform for Simulating Human-like Generative Agents](https://mlsanity.com/paper/2310.05418/) (2023)
- [Large Language Model for Participatory Urban Planning](https://mlsanity.com/paper/2402.17161/) (2024)
- [Simulating Human-like Daily Activities with Desire-driven Autonomy](https://mlsanity.com/paper/2412.06435/) (2024)

## Built on by (most cited first, 8 of 8)

- [A Survey on Agent Workflow -- Status and Future](https://mlsanity.com/paper/2508.01186/) (2025)
- [TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning](https://mlsanity.com/paper/2504.08694/) (2025)
- [Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies](https://mlsanity.com/paper/2503.12613/) (2025)
- [Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applications](https://mlsanity.com/paper/2507.00914/) (2025)
- [Addressing the alignment problem in transportation policy making: an LLM approach](https://mlsanity.com/paper/2510.13139/) (2025)
- [ScaleSim: Serving Large-Scale Multi-Agent Simulation with Invocation Distance-Based Memory Management](https://mlsanity.com/paper/2601.21473/) (2026)
- [CoDesignAI: An AI-Enabled Multi-Agent, Multi-User System for Collaborative Urban Design at the Conceptual Stage](https://mlsanity.com/paper/2603.16008/) (2026)
- [The Wisdom of Agent Crowds: A Human-AI Interaction Innovation Ignition Framework](https://mlsanity.com/paper/2505.06947/) (2025)

## Abstract (arXiv)

Participatory Urban Planning (PUP) is increasingly supported by LLM-based agents, yet existing methods largely rely on static preference elicitation and one-shot stakeholder discussions, overlooking the cyclical nature of real-world planning, where residential life, experience collection, and plan adjustment continually interact. We propose Living-in-the-loop Participatory Urban Planning (LiPUP), a closed-loop paradigm that alternates between simulated residential living and experience-driven plan revision, while posing two key challenges: grounding scattered living experience in concrete urban contexts and translating subjective feedback into spatially coherent planning actions. To instantiate LiPUP, we introduce LiPUP-MA, an LLM-based multi-agent framework that constructs a Plan-centric Graph-based Experience Bank to organize urban-grounded residential feedback from living simulation and equips a Spatially-constrained Skill-augmented Planner agent to revise plans by harmonizing experiential, visual, and geospatial evidence. Experiments show that LiPUP-MA consistently outperforms baselines on both conventional static planning metrics and living-based metrics, while iterative LiPUP cycles further improve plan quality.