PHOENIX: Fine-Tuned SLM-Powered Autonomous Satellite Lifetime Extension via Predictive Self-Healing and Multi-Agent AI Recovery
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
Most CubeSats, small and low-cost satellites roughly the size of a shoebox, do not survive as long as they were designed to: a study of 178 missions found that only 48-65% remain operational after two years, against a designed lifetime of 2-5 years. The deeper issue is that a CubeSat in low Earth orbit (LEO) is physically unreachable from the ground for roughly 85 minutes out of every 96-minute orbit, so faults that start during that window go unnoticed until the next contact pass, by which point recovery may no longer be possible. We propose PHOENIX (Predictive Health On-orbit Edge Neural Intelligence eXtension) to give the satellite its own fault reasoning capability. A fine-tuned Small Language Model (SLM) compact enough to run on embedded hardware is deployed onboard the CubeSat, running on the flight-proven Aethero NxN-ECM computer, monitoring all sensor readings continuously, and resolving recurring faults using a memory system that stores past repairs so the same inference does not need to run twice. Once per orbit it sends a short structured health report to the ground instead of a raw data dump; six specialized AI agents on the ground read that report and generate validated satellite commands within the 5-10 minute contact window. A generative diffusion model (DDPM) creates synthetic training data because real fault examples make up only 0.57-1.80% of the dataset. We report preliminary results on the ESA Anomaly Detection Benchmark (14 years, 76 channels, 118 labeled faults).
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 4 of its 9 references that are in the map. First posted on arXiv 7 Aug 2026.
Builds on
4 of 9 references.
References · 4
- 2025Semantic Caching for Low-Cost LLM Serving: From Offline Learning to Online Adaptationcited by 13
- 2024European Space Agency Benchmark for Anomaly Detection in Satellite Telemetrycited by 12
- 2026Evaluating Large Language Models for Time Series Anomaly Detection in Aerospace Softwarecited by 2
- 2026Deep Learning-Based Anomaly Detection in Spacecraft Telemetry on Edge Devicescited by 1
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- 2026Glass Box at Orbit: A Constitutional AI Verification Framework for Trustworthy Autonomous CubeSat Intelligence
- 2025ASTREA: Introducing Agentic Intelligence for Orbital Thermal Autonomy
- 2026On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations
- 2026SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing
- 2026PTC-Decoder: Towards Intelligent SLMs on Offline Resource-Constrained Edge Devices
- 2025Toward Trusted Onboard AI: Advancing Small Satellite Operations using Reinforcement Learning
- 2026Towards Autonomous Aircraft Surveillance from Nanosatellites through On-Board Inference and Generative Data Augmentation
- 2026Agentic AutoResearch forSpace Autonomy: An Auditable, LLM-Driven Research Agent for Aerospace Control Problems