A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives
🤖 AI Summary
Earth science data are growing rapidly, yet their reusability remains limited. To address this challenge, this work proposes PANGAEA-GPT—a hierarchical multi-agent system featuring a centralized supervisor-worker architecture. The system integrates data-type-aware routing, sandboxed deterministic code execution, and an execution-feedback-driven self-correction mechanism to autonomously orchestrate complex, multi-step analytical workflows. Evaluated in physical oceanography and ecology scenarios, PANGAEA-GPT enables end-to-end data analysis with minimal human intervention, substantially enhancing the discoverability and usability of heterogeneous geoscientific datasets.
Application Category
📝 Abstract
The rapid accumulation of Earth science data has created a significant scalability challenge; while repositories like PANGAEA host vast collections of datasets, citation metrics indicate that a substantial portion remains underutilized, limiting data reusability. Here we present PANGAEA-GPT, a hierarchical multi-agent framework designed for autonomous data discovery and analysis. Unlike standard Large Language Model (LLM) wrappers, our architecture implements a centralized Supervisor-Worker topology with strict data-type-aware routing, sandboxed deterministic code execution, and self-correction via execution feedback, enabling agents to diagnose and resolve runtime errors. Through use-case scenarios spanning physical oceanography and ecology, we demonstrate the system's capacity to execute complex, multi-step workflows with minimal human intervention. This framework provides a methodology for querying and analyzing heterogeneous repository data through coordinated agent workflows.
Problem
Research questions and friction points this paper is trying to address.
data reusability
geoscientific data archives
scalability challenge
underutilized datasets
autonomous discovery
Innovation
Methods, ideas, or system contributions that make the work stand out.
hierarchical multi-agent system
autonomous data discovery
data-type-aware routing
sandboxed code execution
self-correction via feedback
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