为海湾国家气候决策打造了数据与智能代理系统。
GCA Framework: A GCC Countries-Grounded Dataset and Agentic Pipeline for Climate Decision Support
- 构建了覆盖20万问答对的海湾国家气候数据集，融合政策、科研与遥感信息。
- 开发工具增强型智能体，可实时处理地理空间数据并生成可解释可视化结果。
- 实证表明领域微调+工具调用显著提升大模型在本地气候任务中的可靠性。
海湾合作委员会（GCC）国家的气候决策日益需要将异构科学与政策证据转化为可操作建议的系统，但通用大语言模型在区域气候知识和与地理空间及预测工具的交互能力上仍显薄弱。本文提出GCA框架，包含两部分：(i) GCA-DS，一个基于海湾国家的多模态精选数据集；(ii) Gulf Climate Agent（GCA），一种工具增强型智能代理。GCA-DS涵盖20万组问答对，覆盖政府政策与适应计划、非政府组织及国际框架、学术文献，以及针对热浪、沙尘暴和洪水等事件的报道，并整合遥感图像与文本证据。在此基础上，GCA智能体构建模块化工具流水线，依托实时与历史信号及地理空间处理，生成衍生指数与可解释可视化结果。最后，我们在海湾国家气候任务中对比评测了开源与专有大模型，结果表明领域微调与工具集成能显著提升模型可靠性，优于通用基线。
原文摘要 · Abstract (English)
Climate decision-making in the GCC states increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guidance, yet general-purpose large language models (LLMs) remain weak both in region-specific climate knowledge and grounded interaction with geospatial and forecasting tools. We present the GCA framework, which unifies (i) GCA-DS, a curated multimodal dataset grounded in the GCC states, and (ii) Gulf Climate Agent (GCA), a tool-augmented agent for climate analysis. GCA-DS comprises 200k question--answer pairs spanning governmental policies and adaptation plans, NGO and international frameworks, academic literature, and event-driven reporting on heatwaves, dust storms, and floods, complemented with remote-sensing inputs that couple imagery with textual evidence. Building on this foundation, the GCA agent orchestrates a modular tool pipeline grounded in real-time and historical signals and geospatial processing that produces derived indices and interpretable visualizations. Finally, we benchmark open and proprietary LLMs on climate tasks in the GCC states and show that domain fine-tuning and tool integration substantially improve reliability over general-purpose baselines.
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