# ClimAgent: LLM as Agents for Autonomous Open-ended Climate Science Analysis

arXiv: https://arxiv.org/abs/2604.16922 · HTML: https://mlsanity.com/paper/2604.16922/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 18 Apr 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 14 of 14)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Agent Laboratory: Using LLM Agents as Research Assistants](https://mlsanity.com/paper/2501.04227/) (2025)
- [Memory in the Age of AI Agents](https://mlsanity.com/paper/2512.13564/) (2025)
- [Large Language Models for Automated Open-domain Scientific Hypotheses Discovery](https://mlsanity.com/paper/2309.02726/) (2023)
- [MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses](https://mlsanity.com/paper/2410.07076/) (2024)
- [DeepAnalyze: Agentic Large Language Models for Autonomous Data Science](https://mlsanity.com/paper/2510.16872/) (2025)
- [MM-Agent: LLM as Agents for Real-world Mathematical Modeling Problem](https://mlsanity.com/paper/2505.14148/) (2025)
- [Improving Physics Reasoning in Large Language Models Using Mixture of Refinement Agents](https://mlsanity.com/paper/2412.00821/) (2024)
- [ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models](https://mlsanity.com/paper/2410.16701/) (2024)
- [The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding](https://mlsanity.com/paper/2502.08946/) (2025)
- [PhyDA: Physics-Guided Diffusion Models for Data Assimilation in Atmospheric Systems](https://mlsanity.com/paper/2505.12882/) (2025)
- [DAMBench: A Multi-Modal Benchmark for Deep Learning-based Atmospheric Data Assimilation](https://mlsanity.com/paper/2511.01468/) (2025)

## Built on by (most cited first, 2 of 2)

- [SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents](https://mlsanity.com/paper/2607.24588/) (2026)
- [Execution-grounded evaluation reveals hidden failures in language-model calculations for environmental science](https://mlsanity.com/paper/2608.18726/) (2026)

## Abstract (arXiv)

Climate research is pivotal for mitigating global environmental crises, yet the accelerating volume of multi-scale datasets and the complexity of analytical tools have created significant bottlenecks, constraining scientific discovery to fragmented and labor-intensive workflows. While the emergence Large Language Models (LLMs) offers a transformative paradigm to scale scientific expertise, existing explorations remain largely confined to simple Question-Answering (Q&A) tasks. These approaches often oversimplify real-world challenges, neglecting the intricate physical constraints and the data-driven nature required in professional climate science.To bridge this gap, we introduce ClimAgent, a general-purpose autonomous framework designed to execute a wide spectrum of research tasks across diverse climate sub-fields. By integrating a unified tool-use environment with rigorous reasoning protocols, ClimAgent transcends simple retrieval to perform end-to-end modeling and analysis. To foster systematic evaluation, we propose ClimaBench, the first comprehensive benchmark for real-world climate discovery. It encompasses challenging problems spanning 5 distinct task categories derived from professional scenarios between 2000 and 2025. Experiments on ClimaBench demonstrate that ClimAgent significantly outperforms state-of-the-art baselines, achieving a 40.21% improvement over original LLM solutions in solution rigorousness and practicality. Our code are available at https://github.com/usail-hkust/ClimAgent.