# Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts

arXiv: https://arxiv.org/abs/2601.16965 · HTML: https://mlsanity.com/paper/2601.16965/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Placed in

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

## Builds on (most cited first, 7 of 7)

- [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://mlsanity.com/paper/2305.04091/) (2023)
- [An LLM Agent for Automatic Geospatial Data Analysis](https://mlsanity.com/paper/2410.18792/) (2024)
- [MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models](https://mlsanity.com/paper/2501.00316/) (2024)
- [MapQA: Open-domain Geospatial Question Answering on Map Data](https://mlsanity.com/paper/2503.07871/) (2025)
- [Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions](https://mlsanity.com/paper/2502.18470/) (2025)
- [Hierarchical Neural Program Synthesis](https://mlsanity.com/paper/2303.06018/) (2023)
- [Inherent limitations of LLMs regarding spatial information](https://mlsanity.com/paper/2312.03042/) (2023)

## Built on by (most cited first, 7 of 7)

- [NORA: A Harness-Engineered Autonomous Research Agent for End-to-End Spatial Data Science](https://mlsanity.com/paper/2605.02092/) (2026)
- [A Dual-Helix Governance Approach Towards Reliable Agentic Artificial Intelligence for WebGIS Development](https://mlsanity.com/paper/2603.04390/) (2026)
- [ANASSA: An Agentic AI Orchestration Framework for Spatial Intelligence](https://mlsanity.com/paper/2609.14824/) (2026)
- [EarthVerse: Benchmarking Scientific Agents Across Dynamic Earth Systems and Natural Hazards](https://mlsanity.com/paper/2608.23525/) (2026)
- [PolyTopoBench: A Benchmark for Complex Vector Polygon Generation from Remote Sensing Imagery](https://mlsanity.com/paper/2609.32856/) (2026)
- [ANASSA: From High-Level Framework to Low-Level Architecture for Spatial Agentic AI](https://mlsanity.com/paper/2610.14382/) (2026)
- [Trust Before Autonomy: Rethinking Agentic Remote Sensing for Crisis Response](https://mlsanity.com/paper/2610.18789/) (2026)

## Abstract (arXiv)

Geospatial reasoning is essential for real-world applications such as urban analytics, transportation planning, and disaster response. However, existing LLM-based agents often fail at genuine geospatial computation, relying instead on web search or pattern matching while hallucinating spatial relationships. We present Spatial-Agent, an AI agent grounded in foundational theories of spatial information science. Our approach formalizes geo-analytical question answering as a concept transformation problem, where natural-language questions are parsed into executable workflows represented as GeoFlow Graphs -- directed acyclic graphs with nodes corresponding to spatial concepts and edges representing transformations. Drawing on spatial information theory, Spatial-Agent extracts spatial concepts, assigns functional roles with principled ordering constraints, and composes transformation sequences through template-based generation. Extensive experiments on MapEval-API and MapQA benchmarks demonstrate that Spatial-Agent significantly outperforms existing baselines including ReAct and Reflexion, while producing interpretable and executable geospatial workflows.