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评论文章探讨“Math 2.0”应如何更整体地评估数学研究进展，呼吁改变当前以论文为中心的评价体系。
A tweet argues that calling for a slowdown in AI development is a convenient position that ignores the costs to scientific progress, such as delayed cancer treatments and drug discovery.
This essay explores the psychological limits of motivation in scientific discovery, using examples from quantum mechanics and rationality communities to argue that conviction and urgency are key to breakthroughs.
Arbor is an AI framework for autonomous scientific research that uses a coordinator, executors, and a persistent hypothesis tree to iteratively improve research outcomes across multiple domains, achieving strong results on six real research tasks.
A study evaluates frontier models' ability to forecast scientific progress across 4,760 events, finding they can identify plausible directions but cannot reliably predict outcomes or timelines, with systematic overconfidence.
At Google I/O, DeepMind CEO Demis Hassabis announced that scientific progress is becoming computable, launching the Gemini for Science system to help researchers read papers, write code, and generate hypotheses, enabling science to scale and iterate like software.
This paper introduces CUSP, a benchmark for evaluating AI systems' ability to forecast scientific progress, finding that current models show systematic overconfidence and domain-dependent limitations, failing to reliably predict scientific advances.