# Large Language Models as Autonomous Spacecraft Operators in Kerbal Space Program

arXiv: https://arxiv.org/abs/2505.19896 · HTML: https://mlsanity.com/paper/2505.19896/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [DoRA: Weight-Decomposed Low-Rank Adaptation](https://mlsanity.com/paper/2402.09353/) (2024)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games](https://mlsanity.com/paper/1909.04694/) (2019)
- [Language Models are Spacecraft Operators](https://mlsanity.com/paper/2404.00413/) (2024)
- [Visual Language Models as Operator Agents in the Space Domain](https://mlsanity.com/paper/2501.07802/) (2025)

## Built on by (most cited first, 5 of 5)

- [Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous](https://mlsanity.com/paper/2512.09111/) (2025)
- [Intent-aligned Autonomous Spacecraft Guidance via Reasoning Models](https://mlsanity.com/paper/2604.17176/) (2026)
- [GUIDE: Guided Updates for In-context Decision Evolution in LLM-Driven Spacecraft Operations](https://mlsanity.com/paper/2603.27306/) (2026)
- [A Systematic Evaluation of Retrieval-Augmented Generation and Language Models for Space Operations](https://mlsanity.com/paper/2605.27444/) (2026)
- [OrbitTAMP: Grounding Language Models for Task and Motion Planning in Spacecraft Rendezvous](https://mlsanity.com/paper/2610.01093/) (2026)

## Abstract (arXiv)

Recent trends are emerging in the use of Large Language Models (LLMs) as autonomous agents that take actions based on the content of the user text prompts. We intend to apply these concepts to the field of Control in space, enabling LLMs to play a significant role in the decision-making process for autonomous satellite operations. As a first step towards this goal, we have developed a pure LLM-based solution for the Kerbal Space Program Differential Games (KSPDG) challenge, a public software design competition where participants create autonomous agents for maneuvering satellites involved in non-cooperative space operations, running on the KSP game engine. Our approach leverages prompt engineering, few-shot prompting, and fine-tuning techniques to create an effective LLM-based agent that ranked 2nd in the competition. To the best of our knowledge, this work pioneers the integration of LLM agents into space research. The project comprises several open repositories to facilitate replication and further research. The codebase is accessible on hrefhttps://github.com/ARCLab-MIT/kspdgGitHub, while the trained models and datasets are available on hrefhttps://huggingface.co/OhhTuRnzHugging Face. Additionally, experiment tracking and detailed results can be reviewed on hrefhttps://wandb.ai/carrusk/huggingfaceWeights & Biases