AI 中文总结
该研究提出气候专用大型语言模型Fuxi-Climate基础模型，其在跨学科气候推理中性能更稳定，能实现高权衡覆盖度与不确定性感知推理，可为气候风险分析及智能体决策系统提供支撑。
AI 中文摘要
气候研究与决策需要整合物理过程、社会经济动态及政策响应等多领域证据。已有研究探索用大型语言模型（LLMs）访问和综合气候知识，但其支持结构化跨学科推理的能力仍有限。本文提出Fuxi-Climate基础模型（CFM），这是一款专为支持跨领域一致性推理设计的气候专用LLM。当跨学科复杂性提升时，CFM的分析行为保持更稳定，而其他模型的性能则更易波动。在专家设计的气候转型任务上，CFM生成更结构化的分析，明确权衡取舍与不确定性，实现45%的权衡覆盖度和47.27%的不确定性感知推理。这些结果表明，CFM可支持对气候风险与转型路径的更现实分析，并为基于智能体的系统探索复杂政策与决策场景提供基础。该模型可通过此公开链接获取。
英文摘要
Climate research and decision-making require integrating evidence across physical processes, socio-economic dynamics and policy responses. Large language models (LLMs) have been explored for accessing and synthesizing climate knowledge, but their ability to support structured interdisciplinary reasoning is still limited. Here we present the Fuxi-Climate Foundation Model (CFM), a climate-specialized LLM designed to support consistent reasoning across domains. CFM maintains more stable analytical behavior as interdisciplinary complexity increases, whereas performance in other models becomes more variable. On expert-designed climate transition tasks, CFM produces more structured analyses that explicitly address trade-offs and uncertainty, achieving 45% trade-off coverage and 47.27% uncertainty-aware reasoning. These results indicate that CFM can support more realistic analysis of climate risks and transition pathways, and provide a basis for agent-based systems to explore complex policy and decision scenarios. The model is openly available at https://huggingface.co/SII-yuning/cfm.
Comments28 pages, 17 figures
