---
title: "ClimAgent — paper by Wang et al. (2026) — summary and ideas"
description: "To evaluate such systems, the authors also release ClimaBench, a benchmark of real climate events from 2000–2025. 40 references."
authors: ["Hao Wang", "Jindong Han", "Wei Fan", "Hao Liu"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1067/"
doi: "10.18653/v1/2026.findings-acl.1067"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2026-climagent-llm-as-agents-autonomous/"
---
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# ClimAgent: LLM as Agents for Autonomous Open-ended Climate Science Analysis

**Authors:** [Hao Wang](https://md.research.elamur.ai/free/en/authors/hao-wang/), Jindong Han, [Wei Fan](https://md.research.elamur.ai/free/en/authors/wei-fan/) and [Hao Liu](https://md.research.elamur.ai/free/en/authors/hao-liu/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1067/) · [PDF](https://aclanthology.org/2026.findings-acl.1067.pdf) · [DOI 10.18653/v1/2026.findings-acl.1067](https://doi.org/10.18653/v1/2026.findings-acl.1067) · [OpenAlex](https://openalex.org/W7166818636)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Most prior work on LLMs in climate science tests question answering. Real climate research is different: it requires turning a loosely stated scenario into a physically grounded, data-driven analysis. ClimAgent is an autonomous agent built for this open-ended setting. Its foundation is the Climate Environment (CE), a shared action space assembled by surveying hundreds of climate papers across 25 sub-fields. CE gives the agent specialized tools, datasets and equations. The agent works like a domain expert, in four stages:
1. It breaks the problem into sub-tasks.
2. It retrieves the relevant modeling knowledge and refines each sub-task so that physical constraints are kept.
3. It writes and runs code to compute the results.
4. It compiles the results into a LaTeX report.

To evaluate such systems, the authors also release ClimaBench, a benchmark of real climate events from 2000–2025. ClimAgent beats strong agent baselines on ClimaBench, and its scores from automatic judging closely match ratings from human experts. Together, the agent and benchmark give a reproducible starting point for automating climate research.

## Key points
- CE combines 150 climate tools and 30 databases. Its data comes from public sources such as NOAA, ERA5 and NASA Earthdata, mostly under CC-BY or open-government licenses.
- ClimaBench has 220 open-ended problems in 5 task categories. Solving them requires interpreting the problem, formulating a model and doing numerical reasoning.
- ClimAgent improves solution rigor and practicality by 40.21% over plain LLM solutions, at roughly half the token cost of competing state-of-the-art approaches.
- Ablation studies take away three components: the Climate Environment (CE), Problem Analysis (PA) and Climate Modeling (CM). Each removal lowers performance; dropping PA especially hurts AE and MR. In a human check, five climate PhD students rated 100 solutions, and their scores differed from the automatic ones by at most 0.08 per metric.
- The system mainly relies on large backbones (GPT-4o, Qwen3, DeepSeek-R1). The smaller Qwen2.5-72B followed instructions less reliably and produced less rigorous models.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Hong Kong University of Science and Technology](https://md.research.elamur.ai/free/en/universities/hong-kong-university-of-science-and-technology/) — University, Hong Kong, China · 170 works · 26,341 citations
- [The Hong Kong University of Science and Technology (Guangzhou)](https://md.research.elamur.ai/free/en/universities/the-hong-kong-university-of-science-and-technology-guangzhou/) — University, Guangzhou, China · 67 works · 1,765 citations
- [Shandong University](https://md.research.elamur.ai/free/en/universities/shandong-university/) — University, Jinan, China · 19 works · 952 citations
- [University of Auckland](https://md.research.elamur.ai/free/en/universities/university-of-auckland/) — University, Auckland, New Zealand · 15 works · 888 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works

## Cite

Cite as: Hao Wang, Jindong Han, Wei Fan and Hao Liu. 2026. ClimAgent: LLM as Agents for Autonomous Open-ended Climate Science Analysis. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2026climagent,
  title = {{ClimAgent: LLM as Agents for Autonomous Open-ended Climate Science Analysis}},
  author = {Hao Wang, Jindong Han, Wei Fan and Hao Liu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1067},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1067}
}
```

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*Updated 2 November 2026*