GUIDE: Guided Updates for In-context Decision Evolution in LLM-Driven Spacecraft Operations
📰 ArXiv cs.AI
GUIDE framework enables cross-episode adaptation in LLM-driven spacecraft operations without weight updates
Action Steps
- Define a structured, state-conditioned playbook of natural-language decision rules
- Implement a non-parametric policy improvement framework to enable cross-episode adaptation
- Use a lightweight acting model to perform actions based on the evolved playbook
- Evaluate and refine the GUIDE framework through repeated executions and feedback
Who Needs to Know This
AI engineers and researchers working on LLM-driven spacecraft operations can benefit from GUIDE to improve decision-making across repeated executions. This can also be useful for ml-researchers looking to apply LLMs to real-world problems
Key Insight
💡 GUIDE enables LLMs to learn and adapt across repeated executions without requiring weight updates
Share This
🚀 GUIDE framework improves LLM-driven spacecraft ops with cross-episode adaptation!
DeepCamp AI