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01Microsoft Research AI for Science · Microsoft Research Feed
Jennifer Neville is a partner research manager at Microsoft who’s built a career around understanding and advancing AI for real-world use, and much like the human-AI interactions she’s been studying, her early-career path was multiturn: math, then physics; cognitive science, then work; and finally computer science—despite her best efforts to avoid the field. In this conversation with Principal Applied Scientist Chad Atalla , she explores the role evaluation plays in pushing the performance boundaries of today’s AI systems to meet user needs and the “surprising failures” that emerge when models are tested beyond traditional benchmarks. Neville also shares practical guidance for working with current AI systems and discusses why looking closely at data matters when results defy expectations, and what decades of AI progress have taught her about predicting what comes next. From an unexpected career trajectory to the frontier of AI interaction and learning, this episode asks a larger question: what can we learn when the path—whether human or artificial—doesn’t unfold the way we expect? Subscribe to the Microsoft Research Podcast : Apple Podcasts Email Android Spotify RSS Feed Transcript [MUSIC] JENNIFER NEVILLE: There were times that I felt like I was spinning my wheels. Things weren’t working. I would get, kind of, dejected. And then we’d get to a point where we did learn something, and it was just the, the emotional thrill of that was, that was really what hooked me. The being able to, kind of, understand something that no one else understood yet … CHAD ATALLA: Sure … NEVILLE: … because it’s just at the frontier of what we know about things. STANDARD INTRODUCTION: This is the Microsoft Research Podcast , where Microsoft researchers—driving advancement through fundamental science and technology research—explore the who, how, and what’s next in computing and AI. [MUSIC ENDS] CHAD ATALLA: Hello, and welcome. I’m Chad Atalla, an applied scientist here at Microsoft Research. Today, I’m joined by Jennifer Neville, a partner research manager at Microsoft Research and the Samuel D. Conte chair professor of computer science and statistics at Purdue. Her research examines machine learning and AI for interactive domains and structured data, looking at how the data points that AI systems are trained on affect their behaviors and how that aligns with what users actually wa…
Terence Tao
[This is a guest post by Ben Antieau , cross-posted from his blog . — T.] There was no personal problem, no world problem, whose eloquent solution did not exist— somewhere in some hexagon. —Jorge Luis Borges, “The Library of Babel” [1] New repository I am writing to announce Hexagon , a new repository for research works in the mathematical sciences and theoretical computer science. This has been a joint effort of many people over the last couple of months, starting in early August 2026. We felt that the proliferation of results proved with LLM assistance required a new model for collecting the associated papers. Besides wanting these works to be citable and reliably stored for long periods of time, we wanted to allow lower barriers for authorship and for community interest than the arXiv. We also wanted for these works to have a home besides random GitHub repositories or X/Bluesky/Mathstodon/LinkedIn posts. In order to support the development of this project, we founded the Hexagon Mathematics Foundation in the state of Delaware and we intend to apply for recognition of tax-exempt status under section 501(c)(3). The Board of Directors consists of Mohammed Abouzaid, François Charles, Bryna Kra, David Savitt, and Lauren Williams. I serve as the first Executive Director. And, we have a wonderful Advisory Board, where Kevin Buzzard, Akhil Mathew, Johannes Schmitt, Steinn Sigurðsson, Nikhil Srivastava, Ravi Vakil, and Rachel Ward serve. For more details, see our who we are page. My personal interest When working with a group of people brought together by an FRG grant, we clicked a button and discovered a new result, namely derived invariance of Hodge numbers of fourfolds in characteristic zero. In the past, this would be a result I would have submitted to an excellent, but not top, journal. In this case, there is no way I would put my name to the result. Instead, I blogged about it here . This is a brittle way to aggregate mathematics. Now, it is on Hexagon as hexagon:2609.00104 . The creation of new results happens naturally in working with LLMs. They bubble up, even as side results. My personal hope is that it becomes the community standard to identify these and submit them to Hexagon. The Lifecycle of Hexagon Objects (The title alludes to Ted Chiang’s “The Lifecycle of Software Objects” [2].) My ambition is that Hexagon is where mathematical ideas emerge from…
Discovery · OpenAI
The floodgates are open. Right on the heels of OpenAI’s new large language model producing the biggest math breakthrough in two decades, the company just released no less than 372 results from the same model. Each resolves or makes substantial progress on a major open question in mathematics or theoretical computer science, the company says. OpenAI revealed the results in a GitHub repository at 6 P.M. EDT. The deluge will take mathematicians months to parse through and understand—including to determine whether the proofs contain novel and important ideas or are mostly mash-ups of existing techniques. But many of the results have already been verified in Lean—a programming language that validates a proof’s logic—and are therefore all but certain to be correct. Among the results claimed are a solution to the four-dimensional Kakeya conjecture, improvements on some of the world’s most important computer algorithms, and actual progress toward math’s scariest problem, the Riemann hypothesis. On supporting science journalism If you're enjoying this article, consider supporting our award-winning journalism by subscribing. By purchasing a subscription you are helping to ensure the future of impactful stories about the discoveries and ideas shaping our world today. A spokesperson for OpenAI told Scientific American that the new model—which the company has not released to the public—produced almost every one of the results in response to a single prompt handed to a single AI agent. This would be a striking difference from OpenAI’s earlier blockbuster solution to the Navier-Stokes problem, which was produced through the collective efforts of a 10,000-strong agentic swarm that cost millions of dollars in computing power. If true, it would mean that unprecedented mathematical power could soon be accessible to anyone. But OpenAI’s reputation, among mathematicians, for bold claims and little transparency is causing considerable skepticism. “Until and unless they release the model and people can replicate their results, I think you should treat any claims about one-shotting problems with a single agent as unverified,” says Andrew Sutherland, a mathematician at the Massachusetts Institute of Technology. “We should ask for receipts.” (The OpenAI spokesperson also told Scientific American that some results might have taken multiple attempts.) OpenAI’s handling of the Navier-S…
Discovery · OpenAI
OpenAI releases 722 AI-generated mathematics papers as researchers weigh what comes next OpenAI has published hundreds of mathematical manuscripts produced by an unreleased AI model, opening a much bigger debate over how AI-generated research should be checked, understood and incorporated into mathematics OpenAI has released a collection of 722 mathematical manuscripts generated by an internal frontier AI model, covering hundreds of research problems across mathematics and theoretical computer science. The collection is organized into 372 groups of related results and includes proofs, alternative arguments, consequences and supporting material. Many of the results also include formal proofs written in Lean, a programming language that allows mathematical arguments to be checked computationally. OpenAI says the work emerged from testing its internal models on open research problems after performance on its existing mathematics evaluations began to plateau. Around 4,000 problems were presented to the model during the evaluation process. OpenAI says the average result used computing resources equivalent to roughly three hours of ChatGPT Pro thinking. The scale of the release makes it considerably different from a typical AI benchmark. Rather than measuring whether a model can answer a fixed set of questions, OpenAI is publishing work that is intended to enter the mathematical research process itself. But the company is also explicit that publication does not mean every result has already been fully verified. The repository contains work at different stages of checking, and OpenAI says some manuscripts that have not yet been formalized in Lean could contain errors. Corrections and revisions will be recorded while earlier versions remain available. From solving problems to publishing research OpenAI first outlined its approach to releasing AI-generated mathematical results in September, when it said it was working with mathematicians on how such work should be presented responsibly. The company said it wanted to improve transparency around AI-generated mathematics by publishing supporting information alongside the results, including formal proofs where available, reasoning summaries and details about the computational resources used. Ten abridged summaries of the model’s reasoning have also been published for results spanning areas including number theory, compl…
AIhub · Research news and interviews
In a series of interviews, we’re meeting some of the PhD students that were selected to take part in the Doctoral Consortium at the International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) . This time, we hear from Mayra Elwes about her work developing machine learning methods for clinical time-series data. Tell us a bit about your PhD – where are you studying, and what is the topic of your research? I am a second-year computer science PhD student at the Institute for Biomedical Informatics at the University Hospital Cologne. My research focuses on the generalisation and domain adaptation of machine learning for clinical time-series forecasting, especially for clinic-to-clinic generalization scenarios. I am working on practical strategies to evaluate methods in a more contextualised way and on improving existing domain adaptation methods specific to time-series forecasting. Could you give us an overview of the research you’ve carried out during your PhD? One major challenge in medical AI is performance degradation when models are deployed in clinics not represented in their training data. This performance degradation often stems from differences in patient demographics, clinical practice, or measuring devices, and it manifests as data distribution shifts. I address this problem for a use-case from the intensive care unit: forecasting blood oxygenation levels and arterial mean blood pressure based on observed vital parameters to improve hypotension and hypoxemia prediction. Since this application task is not covered by standard AI benchmarks, my clinical collaborators and I designed a new benchmark. Currently, we are collecting data at University Hospital Cologne. Tools and datasets I built to benchmark generalization for this vital parameter forecasting task are available as open-source software： WavePrep , Algorithm2Domain , MIMIC-III Waveform Database Matched Subset . While building this benchmark, I developed a strategy to benchmark generalisation and domain adaptation approaches for application tasks and domains in a more contextualised way. By reporting the shift relevant to the downstream task alongside the performance degradation, results become comparable even on private datasets and between different application tasks. Is there an aspect of your research that has been pa…
