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SciExam for ENSO: Can AI Agents Build Climate Models?

Paper recorded by Signals 4 on 2026-10-07 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-07 on arXiv · recorded by Signals 4 on 2026-10-08

Category: cs.AI · 人工智能 · first seen 2026-10-08

Abstract

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,

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#385 most recent of 960 cs.AI papers we have recorded · ↑ newer: RoboJEPA: Scaling Robotic Latent World Models · ↓ older: RECAST: Learning to Compute the Right Context through Adaptive Evidenc

Cite this page: SciExam for ENSO: Can AI Agents Build Climate Models?: the #385 most recent of 960 cs.AI papers we have recorded (as of 2026-10-07). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/sciexam-for-enso-can-ai-agents-build-climate-models.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json

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