SciExam for ENSO: Can AI Agents Build Climate Models?· SciExam for ENSO: AI代理能否构建气候模型?
Language-model agents are increasingly asked to carry out open-ended scientific research, yet their results are usually graded against a known answer, a rubric, or a language-model reviewer, none of which can tell whether a new scientific model is valid. The AI Science Exam for El Nino-Southern Oscillation (SciExam for ENSO) is a benchmark in which agents build low-order stochastic models of ENSO, the dominant mode of interannual climate variability, from real observations. Within a six-hour budget, agents process the observations, write their own diagnostics, which are then frozen, and develop a model using only these diagnostics as feedback. Hidden graders then test whether the model reproduces ENSO's statistics, recovers unobserved variables, and forecasts held-out years, and score a pu
AI代理能否从观测数据构建有效的气候模型?
- 核心方法
- 通过SciExam for ENSO基准,AI代理需从真实观测数据中处理信息、编写诊断,并据此开发低阶随机气候模型
- 适合谁读
- 研究者
- 要解决的问题
- 如何评估AI代理在开放性科学研究中构建的新科学模型的有效性
- 关键实验
- 未提供
- 主要贡献
- 提出了一种评估AI代理构建科学模型有效性的新基准
- 意义与局限
- 该研究为未来AI在气候科学领域的应用提供了评估标准,但当前仅限于低阶模型,实际气候系统的复杂性可能难以完全捕捉