Can LLMs boost geoscience discovery via multi-agent systems?
Accelerating Earth Science Discovery via Multi-Agent LLM Systems
March 11, 2025
https://arxiv.org/pdf/2503.05854This paper explores using multi-agent systems (MAS) powered by Large Language Models (LLMs) to improve how scientists interact with large, complex geoscience datasets like PANGAEA. It proposes a framework called "PANGAEA GPT" as a practical example.
Key points for LLM-based multi-agent systems:
- Centralized Orchestration: A supervisor agent delegates tasks to specialized sub-agents (e.g., for oceanography, geology) and combines their results.
- Tool Integration: Agents use external tools (e.g., GDAL, NetCDF, Python scripts) for domain-specific tasks.
- Retrieval Augmented Generation (RAG): Agents access curated knowledge bases to improve accuracy and reduce hallucinations.
- Multi-Tier Memory: Short-term memory is used for immediate context, while long-term memory stores extended data in a vector database.
- Specialized Validation Agents: Domain-specific validation ensures the correctness of results, addressing the lack of universal benchmarks.
- Potential for Autonomous Exploration: "Wandering" agents could autonomously analyze data, generate hypotheses, and identify anomalies.