# SenseWalk: Agent-Based Semantic Trajectory Simulation Powered by Large Language Models in Zoned Environments

arXiv: https://arxiv.org/abs/2607.00989 · HTML: https://mlsanity.com/paper/2607.00989/

## 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 16 of its 60 references that are in the map.
- First posted on arXiv 1 Jul 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, 16 of 16)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [ToolACE: Winning the Points of LLM Function Calling](https://mlsanity.com/paper/2409.00920/) (2024)
- [OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agents](https://mlsanity.com/paper/2410.21286/) (2024)
- [PedFormer: Pedestrian Behavior Prediction via Cross-Modal Attention Modulation and Gated Multitask Learning](https://mlsanity.com/paper/2210.07886/) (2022)
- [STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction](https://mlsanity.com/paper/2005.04255/) (2020)
- [TrajLLM: A Modular LLM-Enhanced Agent-Based Framework for Realistic Human Trajectory Simulation](https://mlsanity.com/paper/2502.18712/) (2025)
- [Sensible Agent: A Framework for Unobtrusive Interaction with Proactive AR Agents](https://mlsanity.com/paper/2509.09255/) (2025)
- [GATSim: Urban Mobility Simulation with Generative Agents](https://mlsanity.com/paper/2506.23306/) (2025)
- [PosterMate: Audience-driven Collaborative Persona Agents for Poster Design](https://mlsanity.com/paper/2507.18572/) (2025)
- [Holistic Semantic Representation for Navigational Trajectory Generation](https://mlsanity.com/paper/2501.02737/) (2025)
- [Can Agents Spontaneously Form a Society? Introducing a Novel Architecture for Generative Multi-Agents to Elicit Social Emergence](https://mlsanity.com/paper/2409.06750/) (2024)
- [StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework](https://mlsanity.com/paper/2506.14159/) (2025)
- [RetailOpt: Opt-In, Easy-to-Deploy Trajectory Estimation from Smartphone Motion Data and Retail Facility Information](https://mlsanity.com/paper/2404.12548/) (2024)
- [VirtLab: An AI-Powered System for Flexible, Customizable, and Large-scale Team Simulations](https://mlsanity.com/paper/2508.04634/) (2025)
- [Your Thoughtful Opponent: Embracing Cognitive Conflict with Peer Agent](https://mlsanity.com/paper/2508.06955/) (2025)

## Abstract (arXiv)

Semantic trajectory analysis has recently emerged as an approach for modeling human movement by capturing implicit patterns and behaviors through semantic information (e.g., visitors' profiles and goals) beyond raw spatial paths to better understand why people move in certain ways. However, analyzing semantic trajectories in real-world scenarios remains challenging, as collecting high-quality data is costly and often lacks rich semantic information. Meanwhile, existing simulation tools require substantial technical expertise, which makes them difficult for practitioners to adopt. To address these limitations, the paper proposes SenseWalk, an interactive system that supports simulating semantic trajectories by LLM-powered agents. We develop a simulation workflow that combines LLMs and the social force model to balance physical plausibility and semantic coherence. A user-friendly interface is designed to facilitate users in customizing the simulation configuration and analyzing simulation outputs. We also conduct a quantitative experiment to evaluate the effectiveness of our simulation workflow, and a user study (n=12) to assess the usefulness and efficiency of our system.