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Research questionHow should AI agents be benchmarked for environmental geospatial workflows using structured calls to realistic APIs?Environmental scientists can spend substantial effort preparing and wrangling data, yet agent performance on realistic API-driven geospatial workflows remains poorly characterized. Generic GIS benchmarks may not show whether agents can select and execute the structured tool calls required in practical workflows.
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Latest papersRecent research connected to this question, newest first.GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation ModelsThe benchmark contains 93 tasks across 18 categories and uses an open, self-hostable geospatial API serving three environmental indicators across Spain and Portugal through 16 tools. It evaluates nine frontier and open-weight language models, reporting capability and per-case cost; the evidence is limited to this task set, API, geographic coverage, and model comparison.research paper · Sep 3, 2026
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