Mathematical verification must be a national mission in the AI era, write Patrick Shafto, Ken Ono, and Scott Duke Kominers.

AIhub Coffee Corner: does AI change the way we think? This month we ask whether AI tools are changing the way we think. Joining the conversation this time are: Joydeep Biswas (The University of Texas at Austin), Sanmay Das (Virginia Tech), Rina Dechter (University of California, Irvine), Sabine Hauert (University of Bristol), Michael Littman (Brown University), and Marija Slavkovik (University of Bergen). There's just so much to discuss there. I don't know how to start. First of all, it is definitely changing how we write code, develop code, think about correctness, and work on projects. Once upon a time, implementation used to be bottlenecked by writing the code. We are no longer bottlenecked by writing the code, we are bottlenecked in testing and understanding for correctness. As a researcher, there's a lot of what I would call throwaway tools.

The winners of three International Joint Conferences on Artificial Intelligence (IJCAI) awards have been announced . These three distinctions are: the, the and the . The Research Excellence award is given to a scientist who has carried out a program of research of consistently high quality throughout an entire career yielding several substantial results. The winner of the 2026 Award for Research Excellence is Nicholas R. Jennings, Vice-Chancellor and President of Loughborough University, UK. Professor Jennings is recognized for his seminal contributions to the field of multi-agent systems, including algorithms for multi-agent coordination and the principles of human-agent teamwork, and for his pioneering applications of autonomous agents and multi-agent systems.

Robots walking down the street, surrounded by astounded onlookers, is an increasingly common sight. But these machines aren't yet the do-it-all assistants you'd want working in a kitchen or factory, and a major bottleneck is data. Much like humans, robots learn best by experience. The challenge is that it's labor-intensive and time-consuming to physically teach these machines so many actions across different settings. "One natural idea is to use simulation as a training ground. While there has been significant progress over the last few years in the physics engines that power robotics simulators, one of the remaining challenges has been creating sufficiently rich and diverse simulation content to capture the complexity of the real world," says Russ Tedrake, the Toyota Professor of Electrical Engineering and Computer Science (EECS), Aeronautics and Astronautics, and Mechanical Engineering at MIT, and a principal investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

Is This What Comes After AI Slop? Set in a surveillance state ruled by a masked elite, the novel traces the unlikely romance between the president's son and an assassin hired to kill him. Since H. M. Wolfe uploaded to Amazon as a Kindle ebook, it has been a viral hit. "Did I finish, or did finish me?" a TikTok book influencer gushed . In February, Simon & Schuster paid seven figures to secure the publishing rights to the book and its sequel, which is due out next year.

As they scramble to keep their systems online, AI companies are making things expensive for the rest of us. Large language models such as ChatGPT and Claude are so resource-hungry that tech companies may be purchasing 70 percent of the world's supply of high-end computer memory, causing a shortage. As a result, the prices of computer memory and storage are skyrocketing: Hard drives that I bought for my reporting two years ago for $350 each were $800 when I checked two weeks ago, and are now out of stock. The prices of some laptops have gone up as much as 50 percent, and low-cost computers are being hit the hardest. Affordable entry-level computers may "disappear by 2028" according to one forecast .

Trust in a decision-making system requires both safety guarantees and the ability to interpret and understand its behavior. This is particularly important for learned systems, whose decision-making processes are often highly opaque. Shielding is a prominent model-based technique for enforcing safety in reinforcement learning. However, because shields are automatically synthesized using rigorous formal methods, their decisions are often similarly difficult for humans to interpret. Recently, decision trees became customary to represent controllers and policies.

Side-channel analysis (SCA) poses a real-world threat by exploiting unintentional physical signals to extract secret information from secure devices. Evaluation labs also use the same techniques to certify device security. In recent years, deep learning has emerged as a prominent method for SCA, achieving state-ofthe-art attack performance at the cost of interpretability. Understanding how neural networks extract secrets is crucial for security evaluators aiming to defend against such attacks, as only by understanding the attack can one propose better countermeasures. In this work, we apply mechanistic interpretability to neural networks trained for SCA, revealing how models exploit what leakage in side-channel traces. We focus on sudden jumps in performance to reverse engineer learned representations, ultimately recovering secret masks and moving the evaluation process from blackbox to white-box. Our results show that mechanistic interpretability can scale to realistic SCA settings, even when relevant inputs are sparse, model accuracies are low, and side-channel protections prevent standard input interventions.

Many of the insights hitting soccer pitches today trace back to Jesse Davis and a team of computer scientists open-sourcing tools for some of the sport's trickiest problems. Imagine tuning in to the opening kickoff of a World Cup match and seeing a player intentionally send the ball all the way down the pitch and right out of bounds on the opponent's end. Casual fans might scratch their heads. If you were Jesse Davis, though, you'd know that this play could be a prime setup to score. Davis is a professor of computer science at KU Leuven in Belgium and head of its Sports Analytics Lab, which has been at the vanguard of a data awakening in soccer since its inception more than a decade ago. Though the research group brings machine-learning models to bear on a variety of sports--including basketball, volleyball, and field hockey--nowhere is its impact felt more than on the soccer pitch.

Each year the AAAI recognizes a group of individuals who have made significant, sustained contributions to the field of artificial intelligence by appointing them as Fellows. We're talking to some of the 2026 AAAI Fellows to find out more about their work. In this interview, we chat to Sanmay Das, who was elected as a Fellow . Could you start with a quick introduction, where you work, and your general area of research? Broadly speaking, I work in multiagent systems. I've done a lot of work at the intersection of AI and economics, and over the last decade or so I've thought a lot about projects in the AI for social impact and social good space. In particular, my interest has been in the allocation of scarce societal resources, thinking about how AI can be integrated, and what it tells us about systems where we don't necessarily want full free market resource allocation.