# A Self-Evolving AI Agent System for Climate Science

arXiv: https://arxiv.org/abs/2507.17311 · HTML: https://mlsanity.com/paper/2507.17311/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2025.

## Built on by (most cited first, 9 of 9)

- [InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery](https://mlsanity.com/paper/2602.08990/) (2026)
- [The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement](https://mlsanity.com/paper/2609.11873/) (2026)
- [AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery](https://mlsanity.com/paper/2605.23204/) (2026)
- [SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence](https://mlsanity.com/paper/2512.22334/) (2025)
- [HVR-Met: A Hypothesis-Verification-Replanning Agentic System for Extreme Weather Diagnosis](https://mlsanity.com/paper/2603.01121/) (2026)
- [TianJi:An autonomous AI meteorologist for discovering physical mechanisms in atmospheric science](https://mlsanity.com/paper/2603.27738/) (2026)
- [SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents](https://mlsanity.com/paper/2607.24588/) (2026)
- [TianJi-Environ: An Autonomous AI Scientist for Atmospheric Environmental Research](https://mlsanity.com/paper/2606.07697/) (2026)
- [Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches](https://mlsanity.com/paper/2606.01145/) (2026)

## Abstract (arXiv)

Scientific progress in Earth science depends on integrating data across the planet's interconnected spheres. However, the accelerating volume and fragmentation of multi-sphere knowledge and data have surpassed human analytical capacity. This creates a major bottleneck for discovery, especially in climate science. To address this challenge, we introduce EarthLink, the first self-evolving AI agent system designed as an interactive "copilot" for Earth scientists. Through natural language interaction, EarthLink automates the entire research workflow by integrating planning, code execution, data analysis, and physical reasoning into a unified process that directly addresses this limitation. Beyond efficiency, it exhibits human-like cross-disciplinary analytical ability and achieves proficiency comparable to a junior researcher in expert evaluations on core large-scale climate tasks, including model-observation comparison and climate change understanding. When tasked with an open scientific problem, specifically the discovery of precursors of the Atlantic Ni~no, EarthLink autonomously developed a research strategy, identified sources of predictability, verified its hypotheses with available data, and proposed a physically consistent mechanism. These emerging capabilities enable a new human-AI research paradigm. Scientists can focus on value and result judgments, while AI systems handle complex data analysis and knowledge integration. This accelerates the pace and breadth of discovery in Earth sciences. The system is accessible at our website https://earthlink.intern-ai.org.cn.