At the International Conference on Machine Learning (ICML 2026),, and presented their work Vision Language Models Cannot Reason About Physical Transformation . In this interview, William Yijiang Li tells us more about the research, the method the team used, and the controlled experiments they carried out. What is the topic of the research in your paper and why is it an interesting area for study? Our research asks a fairly fundamental question: do vision-language models actually understand how the physical world changes over time? Modern vision-language models are increasingly being used in areas such as robotics, embodied agents, and video understanding.

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Andreessen Horowitz investor Olivia Moore created an AI-generated TikTok influencer for Bama Rush to test how viewers react to artificial intelligence content.

When you purchase through links in our articles, we may earn a small commission. Microsoft promised a better Windows 11. Microsoft has delivered the Windows 11 26H2 Update to the masses. We issue a report card on how it's lived up to its promises. This was supposed to be the year Microsoft fixed Windows 11.

Anthropic Says It Discovered a Crispr-Like System. "The experiments are still in the queue. The PR is already live," says one expert. Tech executives have long touted the potential for their AI models to accelerate the pace of biology discoveries. Last week, they announced one.

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Anthropic has established a biology research lab in the San Francisco Bay Area to conduct physical experiments, according to Reuters. Eric Kauderer-Abrams, the company's Head of Life Sciences, has confirmed the lab's existence to the news organization. "We believe that to do biology, the final test is still and will be for a while in real lab work," he said. "We absolutely are doing that today..." He also said that while Anthropic is doing lab work in-house, it's also working with external partners when it makes sense.

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more In My Bag Look Up Say More Trending Now Mashable Selects Creator Playbook Back to School Good Connection: Uplifting stories for a digital age Switch Off Mashable Voices Safety Net All Series The idea is simple: Better AI helps build even better AI. Keeping that process under control could be much harder. Olivia Tauber is the deputy editor of digital culture, covering creators, media, movies, beauty, and more. Based in New York, her work has appeared in The New York Times, Vanity Fair, The Cut, Teen Vogue, Complex, and Interview Magazine. She holds a Master's degree in Journalism from NYU and a Bachelor's from the University of Michigan.

A group of AI agents asked to solve a series of math problems split into rival factions--when some cheated, others tried to stop them. That whistleblowing behavior, seen for the first time in a recent experiment run by Google DeepMind, could have implications for alignment researchers trying to keep swarms of autonomous AI agents in line. Researchers at frontier labs hope large swarms of agents working together will speed up the rate of scientific discovery. But their behavior can be unpredictable, as vividly demonstrated in July, when a group of OpenAI agents broke out of a sandboxed environment and hacked into the open-source platform Hugging Face looking for ways to cheat on the test they had been given. In the new study, designed to examine the behavior of large groups of AI agents, DeepMind tasked a swarm of 100 agents with solving a series of 71 complicated math problems. All the agents were prompted to behave like world-class math researchers at a conference. They were assigned different specialties--some were experts in number theory, others in combinatorics (a branch of math to do with counting and sorting), analysis, or algebra. All were told to cooperate and play by the rules.

A new report from the nonprofit Reflective lays out the experiments needed to better understand our ability to dim the sun. A San Francisco nonprofit has published a detailed road map of the experiments, studies, and infrastructure that it says would be needed to make informed decisions about the use of solar geoengineering, can reveal. Scientists have now spent half a century exploring the possibility that we could counteract climate change by releasing reflective particles into the stratosphere, mimicking the cooling effects of volcanic eruptions. But even after at least hundreds of studies on the concept, known as stratospheric aerosol injection (SAI), big gaps remain in the scientific understanding of how well it would work and what else it might do--and there has been no systematic plan for clearing up that uncertainty. Reflective, a research organization that funds studies on solar geoengineering, has today attempted to fill that gap with the release of its SAI Research Roadmap . "Our mission is to equip the world with the data and tools required for informed decision-making about sunlight reflection fast enough to matter," says Dakota Gruener, the organization's cofounder and chief executive.