# Automatic Control With Human-Like Reasoning: Exploring Language Model Embodied Air Traffic Agents

arXiv: https://arxiv.org/abs/2409.09717 · HTML: https://mlsanity.com/paper/2409.09717/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 15 Sep 2024.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 4 of 4)

- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [AviationGPT: A Large Language Model for the Aviation Domain](https://mlsanity.com/paper/2311.17686/) (2023)
- [CHATATC: Large Language Model-Driven Conversational Agents for Supporting Strategic Air Traffic Flow Management](https://mlsanity.com/paper/2402.14850/) (2024)

## Built on by (most cited first, 3 of 3)

- [Towards Automated Air Traffic Safety Assessment Around Non-Towered Airports Using Large Language Models](https://mlsanity.com/paper/2605.12332/) (2026)
- [Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems](https://mlsanity.com/paper/2603.28561/) (2026)
- [Air Traffic Control Using Large Language Models: Prompt Engineering, Architecture, and Evaluation](https://mlsanity.com/paper/2608.19299/) (2026)

## Abstract (arXiv)

Recent developments in language models have created new opportunities in air traffic control studies. The current focus is primarily on text and language-based use cases. However, these language models may offer a higher potential impact in the air traffic control domain, thanks to their ability to interact with air traffic environments in an embodied agent form. They also provide a language-like reasoning capability to explain their decisions, which has been a significant roadblock for the implementation of automatic air traffic control. This paper investigates the application of a language model-based agent with function-calling and learning capabilities to resolve air traffic conflicts without human intervention. The main components of this research are foundational large language models, tools that allow the agent to interact with the simulator, and a new concept, the experience library. An innovative part of this research, the experience library, is a vector database that stores synthesized knowledge that agents have learned from interactions with the simulations and language models. To evaluate the performance of our language model-based agent, both open-source and closed-source models were tested. The results of our study reveal significant differences in performance across various configurations of the language model-based agents. The best-performing configuration was able to solve almost all 120 but one imminent conflict scenarios, including up to four aircraft at the same time. Most importantly, the agents are able to provide human-level text explanations on traffic situations and conflict resolution strategies.