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ClimateAgent framework automates climate data science workflows

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ClimateAgent, a multi-agent framework designed to automate complex climate data science workflows. This system decomposes user questions into subtasks, dynamically acquires data through specialized agents, and generates analyses and reports with a self-correction loop. In evaluations on the Climate-Agent-Bench-85 benchmark, ClimateAgent achieved 100% task completion and a higher report quality score than GitHub Copilot and a GPT-5 baseline. AI

IMPACT This framework could significantly accelerate climate research by automating complex data analysis and reporting tasks.

RANK_REASON The cluster describes a new research paper detailing a novel framework for climate data science. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ClimateAgent framework automates climate data science workflows

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The cluster describes a new research paper detailing a novel framework for climate data science. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chenyue Li, Hyeonjae Kim, Wen Deng, Mengxi Jin, Wen Huang, Mengqian Lu, Binhang Yuan ·

    CLIMATEAGENT: Multi-Agent Orchestration for Complex Climate Data Science Workflows

    arXiv:2511.20109v2 Announce Type: replace Abstract: Climate science demands automated workflows to transform comprehensive questions into data-driven statements across massive, heterogeneous datasets. However, generic LLM agents and static scripting pipelines lack climate-specifi…

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