智能体
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基于智能体的 ML-LLM 融合与自优化提示用于高原天气预警
论文提出 SmartWeatherAgent，一个整合意图识别、灾害预测与推理增强生成的三阶段架构，将规则方法与 LLM 结合，并用加入高原特有特征（如风速突变率）的 LightGBM 预测大风、降水和低温事件，报告 F1-Macro 0.605、延迟 1.60 ms。系统通过 12 轮微步提示自优化，将综合预警质量分从 4.2 提升至 8.9，并生成包含因果机制、时空演变、量化证据、法规引用与置信度声明的结构化预警。
AI推荐理由 arXiv 新投稿，提出 SmartWeatherAgent 三阶段架构，融合规则方法与 LLM，用带高原特征的 LightGBM 做灾害预测，报告 F1-Macro 0.605、1.60 ms 延迟，并通过 12 轮微步提示自优化把综合预警质量分从 4.2 提升到 8.9。具体新信息是高原气象场景的 ML-LLM 融合与提示自优化流程及量化指标；局限是仅摘要自述，未见第三方复现，F1 绝对值不高，落地效果未验证。
72
正文
Computer Science > Artificial Intelligence
arXiv:2609.10135 (cs)
[Submitted on 9 Sep 2026]
Title:Agent-Based ML-LLM Fusion with Self-Optimizing Prompts for Plateau Weather Alerts
Authors:Shuai Yan, Yang Xu, Shan He
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Abstract:To address insufficient contextualization, weak generalization, and poor scenario adaptation in tourism meteorological services, we propose SmartWeatherAgent--a unified three-stage architecture integrating intent recognition, hazard prediction, and reasoning-enhanced generation. The system fuses rule-based methods with large language models to parse queries at multiple granularities and employs a LightGBM model enriched with highland-specific features (e.g., wind speed abruptness rate), achieving an F1-Macro score of 0.605 with 1.60 ms latency on high-wind, precipitation, and low-temperature events. A 12-round micro-step prompt self-optimization loop boosts the composite warning quality score S_final from 4.2 (B01) to 8.9 (B12, +112%). Key improvements include a sharp rise in B08 from data source citation (6.5 -> 8.5), sustained high performance in B10 via physical mechanism explanation, and a peak scientific rigor score of 9.2 in B12 through explicit uncertainty statements. The system autonomously generates structured warnings that integrate causal mechanisms, spatiotemporal evolution, quantitative evidence, regulatory references, and confidence statements--enhancing professional depth, logical rigor, and scientific soundness, and advancing meteorological services toward proactive perception, explainable decision-making, and intelligent agency.
Comments:
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
ACM classes:
I.2.4
Cite as:
arXiv:2609.10135 [cs.AI]
(or
arXiv:2609.10135v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.10135
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Shuai Yan [view email]
[v1]
Wed, 9 Sep 2026 13:15:46 UTC (4,556 KB)
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