arXiv:2606.13148v1 Announce Type: new Abstract: Climate and environmental decision-making increasingly requires reasoning across heterogeneous inputs, including gridded physical data, satellite imagery, geospatial context, and simulator outputs. Weather and climate foundation models can forecast well, but do not reason interactively in language, while large language models (LLMs) reason in language but cannot operate directly on high-dimensional Earth-system data. As a result, real scientific workflows in Earth-science remain underserved. We introduce TerraBench, a benchmark for grounded Earth
The proliferation of specialized foundation models and LLMs, coupled with increasing demand for climate intelligence, necessitates benchmarks for evaluating agentic reasoning over complex scientific data.
This benchmark addresses a critical gap in enabling AI agents to interactively reason with and integrate diverse Earth-system data, crucial for scientific workflows and decision-making.
The introduction of TerraBench provides a standardized method to assess and drive the development of AI agents capable of handling heterogeneous scientific inputs, potentially accelerating climate and environmental intelligence.
- · AI agent developers
- · Climate scientists
- · Environmental data providers
- · Generative AI platforms
- · Traditional isolated data analysis methods
- · Organizations relying solely on human interpretation of complex datasets
Improved performance of AI agents in integrating and reasoning over diverse Earth-system data.
Accelerated development of AI tools for climate modeling, environmental monitoring, and disaster prediction.
Enhanced global capacity for climate adaptation and mitigation strategies through more sophisticated AI-driven insights.
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Read at arXiv cs.AI