The AI-based program AlphaFold predicts a protein's 3D structure with remarkable accuracy. However, it tends to reduce heterogeneous structures to a single dominant conformation, or shape, and overlooks experimental conditions that can alter local structure. Researchers at the Institute of Science and Technology Austria (ISTA) and international collaborators have now developed a way to guide AlphaFold with experimental data. Their approach, published in, paves the way for improved future predictive models. Our understanding of molecular structures is considerably influenced by X-ray crystallography--a technique that has served as structural biology's central pillar for many decades.

Strolling through a forest, you may notice that the air is filled with sound. Birds sing, insects and small mammals rustle through the undergrowth, and at dusk bats squeak as they communicate with each other. These soundscapes contain a wealth of information about which animals are present, how many there are and how healthy an ecosystem may be. But analysing all that audio is a huge challenge. Scientists can now collect enormous quantities of recordings using small autonomous devices placed in forests, wetlands and urban areas.

How can we characterize consensus in a network of agents? Imagine a set of artificial agents, expert systems, or decision-makers, each beginning with their own "beliefs" about a shared situation. For example, one transport agent may believe that there is a train strike, another may believe that there is no strike, and a third may believe that if there is a strike, then buses will be overcrowded. They exchange messages over a directed network whose edges represent influence: one agent revises its beliefs after hearing from another, then a different pair communicates, and so on. If influence can eventually travel from every agent to every other agent, will the group end up agreeing?

A photograph of Earth glowing in deep space, the Moon's cratered horizon stretching across its foreground, caught many people's eyes in April 2026. Astronauts captured the image while aboard NASA's Artemis II mission, and like the famous Apollo 8 "Earthrise" image, the picture felt instantly real and inspiring for many. But when almost anyone can fabricate a visually similar image in seconds from a text prompt using artificial intelligence, how do people decide which image is real? The proliferation of AI-generated science images in public spaces is not simply a misinformation problem. As a researcher who studies visual science communication and public trust, I believe it also contributes to a crisis of trust in science in the age of AI, and the tools scientists have long relied on to establish visual credibility are losing their grip.

Diffusion Large Language Models (dLLMs) break the rigid left-to-right constraint of traditional LLMs, enabling token generation in arbitrary orders. Intuitively, this flexibility implies a solution space that strictly supersets the fixed autoregressive trajectory, theoretically unlocking superior reasoning potential. Indeed, for specific constraint satisfaction tasks (e.g., sudoku puzzles), this capability has proven to be highly advantageous. However, in this paper, we reveal that for general reasoning tasks (e.g., mathematics and coding), arbitrary order generation may in fact limit the reasoning potential of dLLMs. We find that dLLMs tend to exploit this order flexibility to bypass high-uncertainty tokens that are crucial for exploration, leading to a premature collapse of solution coverage. This observation motivates a rethink of RL approaches for dLLMs, where considerable complexities, such as handling combinatorial trajectories and intractable likelihoods, are often devoted to preserving this flexibility. We demonstrate that effective reasoning can be better elicited by simply forgoing arbitrary order and applying standard Group Relative Policy Optimization (GRPO) instead. Our approach, JustGRPO, is minimalist yet surprisingly effective (e.g., 89.1% accuracy on GSM8K) while fully retaining the parallel decoding ability of dLLMs.

Imagine you want to teach a robot to push an object on a table. The standard recipe in robot learning is to collect hundreds of expert demonstrations on a real robot, train an imitation learning policy on that data, and then evaluate the policy by running it many times on the same real robot. Both stages (data collection and evaluation) are slow, expensive, and hard to reproduce: hardware breaks, lighting changes, objects drift out of place, and every new task means more hours in the lab. A natural question is whether we can replace some of this real-robot work with a simulator. Classical physics-based simulators are powerful, but building one for a new task means manually modeling geometries, contacts, friction, and deformation, and the resulting simulator often still does not match reality closely enough for policies trained inside it to transfer.

The forty-third International Conference on Machine Learning (ICML) took place in Seoul, South Korea from 6-11 July. We take a look at what the participants got up to during the event. The PC Chairs are presenting the welcome remarks. One of the best conference dinners I've ever had. Try TimeChat-Captioner (at #ICML2026) -- a videoLLM that generates dense, time-aware captions for long videos.

On the 31st March, our editorial team headed to the Royal Society for AI for Science . This day-long conference explored how AI is changing the nature of scientific discovery, and was hosted by the Alan Turing Institute. The recordings from the event are now available on YouTube and are well worth a watch. You can read Ella Scallan's blog post about the day here . Lucy Smith is Senior Managing Editor for AIhub.

The Future of AI Research report, published in March 2025, aims to clearly identify the trajectory of AI research in a structured way. The report was led by outgoing AAAI President Francesca Rossi and covers 17 different AI topics . Members of the report team, and other selected AI practitioners, are taking part in a series of video panel discussions covering selected chapters from the report. In the sixth discussion in the collection, the three panellists tackle factuality and trustworthiness. Understanding factuality: why preventing false outputs from large language models remains AI's toughest problem Lucy Smith is Senior Managing Editor for AIhub.

People often make decisions through "satisficing," gathering just enough information to make a satisfactory prediction of a likely outcome. A series of experimental games shows that people also employ satisficing to learn social rules and conventions. This finding offers new insight into social learning and reveals a key difference between how humans and LLMs make predictions. The premise of AI large language models is that any problem can be solved by vacuuming up as much information as possible, running it through probability models, and performing complex calculations to make predictions and come up with the optimal solution. Another premise behind LLMs is that they emulate the way human brains operate.