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[2602.14017] S2SServiceBench: A Multimodal Benchmark for Last-Mile S2S Climate Services
Summary
The paper presents S2SServiceBench, a multimodal benchmark designed to enhance the effectiveness of last-mile subseasonal-to-seasonal (S2S) climate services by evaluating the performance of multimodal large language models (MLLMs) across various application domains.
Why It Matters
This research addresses the critical gap in translating scientific climate forecasts into actionable services, which is essential for climate resilience and sustainability. By benchmarking MLLMs, the study seeks to improve decision-making processes in sectors affected by climate variability, thus contributing to better preparedness and response strategies.
Key Takeaways
- S2SServiceBench evaluates MLLMs' capabilities in generating actionable climate service deliverables.
- The benchmark covers 10 service products across six domains, providing a comprehensive assessment framework.
- Persistent challenges include understanding actionable signals and operationalizing uncertainty in decision-making.
- The study offers guidance for developing future climate-service agents to enhance decision-making under uncertainty.
- Improving last-mile climate services can significantly impact sectors like agriculture, health, and disaster management.
Computer Science > Machine Learning arXiv:2602.14017 (cs) [Submitted on 15 Feb 2026] Title:S2SServiceBench: A Multimodal Benchmark for Last-Mile S2S Climate Services Authors:Chenyue Li, Wen Deng, Zhuotao Sun, Mengxi Jin, Hanzhe Cui, Han Li, Shentong Li, Man Kit Yu, Ming Long Lai, Yuhao Yang, Mengqian Lu, Binhang Yuan View a PDF of the paper titled S2SServiceBench: A Multimodal Benchmark for Last-Mile S2S Climate Services, by Chenyue Li and 11 other authors View PDF Abstract:Subseasonal-to-seasonal (S2S) forecasts play an essential role in providing a decision-critical weeks-to-months planning window for climate resilience and sustainability, yet a growing bottleneck is the last-mile gap: translating scientific forecasts into trusted, actionable climate services, requiring reliable multimodal understanding and decision-facing reasoning under uncertainty. Meanwhile, multimodal large language models (MLLMs) and corresponding agentic paradigms have made rapid progress in supporting various workflows, but it remains unclear whether they can reliably generate decision-making deliverables from operational service products (e.g., actionable signal comprehension, decision-making handoff, and decision analysis & planning) under uncertainty. We introduce S2SServiceBench, a multimodal benchmark for last-mile S2S climate services curated from an operational climate-service system to evaluate this capability. S2SServiceBenchcovers 10 service products with about 150+ expert-selected case...
