AI-Driven Discovery for Arctic SAI: From Policy Insights to Autonomous Research Frameworks

This session addresses the urgent need for innovative approaches to Arctic climate repair, focusing on the potential of Stratospheric Aerosol Injection (SAI) as a critical intervention strategy. Recognizing the complexities and controversies surrounding SAI deployment, we present a two-pronged AI-driven methodology designed to accelerate both our understanding of existing research and the discovery of novel insights.

First, we will showcase our AI Policy Advisor, a specialized framework leveraging a Stanford-developed large reasoning model fine-tuned on a comprehensive corpus of literature pertaining to SAI and Arctic climate dynamics. This tool demonstrates the power of AI in systematically analyzing and synthesizing the vast body of knowledge, identifying key parameters for potential Arctic SAI deployment, evaluating discussed risks and benefits, and consolidating diverse perspectives on governance and societal implications. By efficiently mining and connecting disparate findings within the existing scientific discourse, the AI Policy Advisor offers a pathway to rapidly generate valuable insights that can inform near-term research directions and policy considerations for Arctic climate intervention.

Second, we will demonstrate the new version of Sakana AI's "AI Scientist" framework, highlighting its capabilities in enabling autonomous scientific discovery. Drawing inspiration from the original AI Scientist which has shown promise in generating novel research ideas, designing experiments, and producing scientific papers in other domains, we will explore its potential application to the complexities of Arctic SAI research. This demonstration will illustrate how such a framework can autonomously formulate research hypotheses, propose experimental avenues based on existing knowledge, and potentially accelerate the identification of promising SAI strategies relevant to the unique challenges of the Arctic.

Concluding the session, we will briefly outline future research directions that build upon these AI-driven foundations. This includes exploring the integration of more sophisticated autonomous research frameworks. We will also touch upon the long-term vision of AI-informed strategies for real-time, adaptive control of SAI processes in the Arctic.