A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives

• URL: http://arxiv.org/abs/2602.21351v1
• Date: Tue, 24 Feb 2026 20:37:38 GMT
• Title: A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives
• Authors: Dmitrii Pantiukhin, Ivan Kuznetsov, Boris Shapkin, Antonia Anna Jost, Thomas Jung, Nikolay Koldunov,
• Abstract summary: PANGAEA-GPT is a hierarchical multi-agent framework designed for autonomous data discovery and analysis.<n>Unlike standard Large Language Model (LLM) wrappers, our architecture implements a centralized Supervisor-Worker topology.<n>We demonstrate the system's capacity to execute complex, multi-step deterministic runtime with minimal human intervention.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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