# AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Prediction

arXiv: https://arxiv.org/abs/2408.13986 · HTML: https://mlsanity.com/paper/2408.13986/

## Key facts

- Cited by 25 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 20 of its 53 references that are in the map.
- First posted on arXiv 26 Aug 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, 20 of 20)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [A Survey on the Memory Mechanism of Large Language Model based Agents](https://mlsanity.com/paper/2404.13501/) (2024)
- [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://mlsanity.com/paper/2402.01622/) (2024)
- [Language Models Represent Space and Time](https://mlsanity.com/paper/2310.02207/) (2023)
- [Scaling Large Language Model-based Multi-Agent Collaboration](https://mlsanity.com/paper/2406.07155/) (2024)
- [MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases](https://mlsanity.com/paper/2402.14905/) (2024)
- [Where Would I Go Next? Large Language Models as Human Mobility Predictors](https://mlsanity.com/paper/2308.15197/) (2023)
- [Large Language Models are Geographically Biased](https://mlsanity.com/paper/2402.02680/) (2024)
- [UrbanGPT: Spatio-Temporal Large Language Models](https://mlsanity.com/paper/2403.00813/) (2024)
- [LLMLight: Large Language Models as Traffic Signal Control Agents](https://mlsanity.com/paper/2312.16044/) (2023)
- [GeoLLM: Extracting Geospatial Knowledge from Large Language Models](https://mlsanity.com/paper/2310.06213/) (2023)
- [CityGPT: Empowering Urban Spatial Cognition of Large Language Models](https://mlsanity.com/paper/2406.13948/) (2024)
- [Large Language Models for Spatial Trajectory Patterns Mining](https://mlsanity.com/paper/2310.04942/) (2023)
- [CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks](https://mlsanity.com/paper/2406.13945/) (2024)
- [Large Language Models are Zero-Shot Next Location Predictors](https://mlsanity.com/paper/2405.20962/) (2024)
- [LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction](https://mlsanity.com/paper/2304.14343/) (2023)
- [Deciphering Human Mobility: Inferring Semantics of Trajectories with Large Language Models](https://mlsanity.com/paper/2405.19850/) (2024)

## Built on by (most cited first, 25 of 25)

- [Understanding World or Predicting Future? A Comprehensive Survey of World Models](https://mlsanity.com/paper/2411.14499/) (2024)
- [Graph World Models: Concepts, Taxonomy, and Future Directions](https://mlsanity.com/paper/2604.27895/) (2026)
- [NextLocLLM: Location Semantics Modeling and Coordinate-Based Next Location Prediction with LLMs](https://mlsanity.com/paper/2410.09129/) (2024)
- [TrajFlow: Nation-wide Pseudo GPS Trajectory Generation with Flow Matching Models](https://mlsanity.com/paper/2603.15009/) (2026)
- [ELLMob: Event-Driven Human Mobility Generation with Self-Aligned LLM Framework](https://mlsanity.com/paper/2603.07946/) (2026)
- [Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks](https://mlsanity.com/paper/2505.11239/) (2025)
- [Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction](https://mlsanity.com/paper/2505.24597/) (2025)
- [A Study on Individual Spatiotemporal Activity Generation Method Using MCP-Enhanced Chain-of-Thought Large Language Models](https://mlsanity.com/paper/2506.10853/) (2025)
- [What Would an LLM Do? Evaluating Large Language Models for Policymaking to Alleviate Homelessness](https://mlsanity.com/paper/2509.03827/) (2025)
- [When Ads Become Profiles: Uncovering the Invisible Risk of Web Advertising at Scale with LLMs](https://mlsanity.com/paper/2509.18874/) (2025)
- [Modeling realistic human behavior using generative agents in a multimodal transport system: Software architecture and Application to Toulouse](https://mlsanity.com/paper/2510.19497/) (2025)
- [ARMove: Learning to Predict Human Mobility through Agentic Reasoning](https://mlsanity.com/paper/2604.17419/) (2026)
- [Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent](https://mlsanity.com/paper/2606.05130/) (2026)
- [A Foundational Individual Mobility Prediction Model based on Open-Source Large Language Models](https://mlsanity.com/paper/2503.16553/) (2025)
- [TS-Mob: Social and Geographical-Aware Time Series Foundation-Model Framework for Human Mobility Prediction](https://mlsanity.com/paper/2507.00945/) (2025)
- [Efficient Temporal Tokenization for Mobility Prediction with Large Language Models](https://mlsanity.com/paper/2507.14017/) (2025)
- [STRelay: A Universal Spatio-Temporal Relaying Framework for Location Prediction over Human Trajectory Data](https://mlsanity.com/paper/2508.16620/) (2025)
- [Capturing Context-Aware Route Choice Semantics for Trajectory Representation Learning](https://mlsanity.com/paper/2510.14819/) (2025)
- [Think over Trajectories: Leveraging Video Generation to Reconstruct GPS Trajectories from Cellular Signaling](https://mlsanity.com/paper/2603.26610/) (2026)
- [ActivityEditor: Learning to Synthesize Physically Valid Human Mobility](https://mlsanity.com/paper/2604.05529/) (2026)
- [TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control](https://mlsanity.com/paper/2604.17456/) (2026)
- [TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond](https://mlsanity.com/paper/2605.06906/) (2026)
- [GPS-Enhanced Tourist Mobility Modeling with Seasonal Spatial Priors and LLM-Based Activity Chain Generation](https://mlsanity.com/paper/2605.29578/) (2026)
- [An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark](https://mlsanity.com/paper/2608.27840/) (2026)
- [BER-PEF: Unified Human Mobility Predictability Evaluation via Bayes Error Rate Estimation](https://mlsanity.com/paper/2609.04292/) (2026)

## Abstract (arXiv)

Next location prediction plays a crucial role in various real-world applications. Recently, due to the limitation of existing deep learning methods, attempts have been made to apply large language models (LLMs) to zero-shot next location prediction task. However, they directly generate the final output using LLMs without systematic design, which limits the potential of LLMs to uncover complex mobility patterns and underestimates their extensive reserve of global geospatial knowledge. In this paper, we introduce AgentMove, a systematic agentic prediction framework to achieve generalized next location prediction. In AgentMove, we first decompose the mobility prediction task and design specific modules to complete them, including spatial-temporal memory for individual mobility pattern mining, world knowledge generator for modeling the effects of urban structure and collective knowledge extractor for capturing the shared patterns among population. Finally, we combine the results of three modules and conduct a reasoning step to generate the final predictions. Extensive experiments utilizing mobility data from two distinct sources reveal that AgentMove surpasses the leading baseline by 3.33% to 8.57% across 8 out of 12 metrics and it shows robust predictions with various LLMs as base and also less geographical bias across cities. Our codes are available via https://github.com/tsinghua-fib-lab/AgentMove.