Ten advances in mathematics and theoretical computer science

OpenAI shares new results on long-standing open problems in mathematics and theoretical computer science, including advances in geometry, cryptography, and complexity.

AI helped produce two proofs for the same cryptography problem

Last Thursday Massachusetts Institute of Technology graduate student Seyoon Ragavan sent a new quantum cryptography proof to his friend Yao-Ting Lin, a doctoral student at the University of California...

The Math Superstar Who’s Terrified of AI—and Just Took a Job at OpenAI

Jacob Tsimerman won the biggest prize in math. Now he’s working on the most important problem of his career.

Can AI agents conduct open-ended AI research? Early evidence from two case studies

Forecasts of explosive AI progress hinge on AI agents automating AI research. But evidence on whether agents can carry out open-ended AI research is thin. Current evaluations either test agents on narrow, verifiable tasks, which excludes open-ended research, or submit AI-generated papers to blind peer review, which is overstretched, stochastic, and suffers from poor review quality. We introduce a third way to measure progress towards AI R\&D automation. An agent takes on the central, open-ended research question of a high-quality unpublished paper, and the paper's original authors grade its output. We call these shadow evaluations. We ran shadow evaluations on two unpublished NeurIPS 2026 submissions, giving frontier agents six days and thousands of dollars of compute. The agents completed all of the engineering without human help, yet could not make substantial progress towards answering the research questions. As a result, both papers were unambiguously rejected by the authors. We identify five recurring failure modes: poor judgment about the bar for publishable research, uncreative responses to shortcomings in the research design, ineffective backtracking from dead ends, poor resource awareness, and instruction drift. A robustness check with a second model and scaffold reproduced these failures. We release the expert reviews, survey responses, agent repositories, and logs. Our results provide early evidence that today's agents can do the engineering of AI research, but struggle with critical parts of the research lifecycle.

Introducing Elicit Research Agent: Rigor that powers decisions - Elicit

Elicit Research Agent is an AI environment built for high-stakes decisions

Guest Post - Do we need (r)evolution in peer review? - The Scholarly Kitchen

Whether peer review needs revolution or evolution may be the wrong question. The more pressing one is whether the academic community is prepared to let scientific ideas be published, contested, and refined in public, rather than behind closed doors.

The Published Voice: Whose Voice Are We Really Reading? - The Scholarly Kitchen

What happens then to “author voice,” which has always been an important consideration to authors and reviewers alike, if AI is helping researchers with their writing?