# Beyond the Townhall: Spatial Anchoring and LLM Agents for Scalable Participatory Urban Planning

arXiv: https://arxiv.org/abs/2604.16348 · HTML: https://mlsanity.com/paper/2604.16348/

## 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 12 of its 152 references that are in the map.
- First posted on arXiv 16 Mar 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, 12 of 12)

- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [Participation is not a Design Fix for Machine Learning](https://mlsanity.com/paper/2007.02423/) (2020)
- [On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trial](https://mlsanity.com/paper/2403.14380/) (2024)
- [LLM Voting: Human Choices and AI Collective Decision Making](https://mlsanity.com/paper/2402.01766/) (2024)
- [A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies](https://mlsanity.com/paper/2505.00036/) (2025)
- [Submitting surveys via a conversational interface: an evaluation of user acceptance and approach effectiveness](https://mlsanity.com/paper/2003.02537/) (2020)
- [The CitizenQuery Benchmark: A Novel Dataset and Evaluation Pipeline for Measuring LLM Performance in Citizen Query Tasks](https://mlsanity.com/paper/2602.04064/) (2026)
- [A Replica for our Democracies? On Using Digital Twins to Enhance Deliberative Democracy](https://mlsanity.com/paper/2504.07138/) (2025)
- [WeDesign: Generative AI-Facilitated Community Consultations for Urban Public Space Design](https://mlsanity.com/paper/2508.19256/) (2025)
- [Learn, Explore and Reflect by Chatting: Understanding the Value of an LLM-Based Voting Advice Application Chatbot](https://mlsanity.com/paper/2505.09806/) (2025)
- [Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Making](https://mlsanity.com/paper/2509.19643/) (2025)
- [Learning Through Dialogue: Engagement and Efficacy Matter More Than Explanations](https://mlsanity.com/paper/2601.07796/) (2026)

## Abstract (arXiv)

LLMs are increasingly explored for public participation, but less is known about how they should be designed around the information citizens encounter beforehand. In an experiment (N=200) on participatory urban planning, participants learned about a sustainability project adapted from a real street redesign, through text and images or narrated 360-degree video with spatial audio and spatial anchoring; they subsequently engaged with factual and reflective LLM personas. The immersive condition improved recall and changed what participants said to the agents: they named beneficiaries more often, asked directionally more project-related questions, and provided more positive feedback. We contribute (1) a conceptualization of LLMs as dialogue partners complementing a shared information baseline, (2) causal evidence that this baseline shapes those conversations, (3) an account of how citizens use and judge factual versus reflective agent roles, and (4) a method for cities to test information packages and agent configurations before public release.