The 35th International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) distinguished paper awards recognise some of the best papers presented at the conference each year. This year, three articles were named as distinguished papers . In approval-based budget division, the task is to allocate a divisible resource to the candidates based on the voters' approval preferences over the candidates. For this setting, Brandl et al. [2021] have shown that no distribution rule can be strategyproof, efficient, and fair at the same time. In this paper, we aim to circumvent this impossibility theorem by focusing on approximate strategyproofness.

On Rashomon sets, the mathematics of simplicity, and why we don't need black boxes: an interview with Cynthia Rudin Welcome back to AI Pioneers - in-depth conversations with those shaping the field . This time, we speak with Cynthia Rudin, a trailblazer in the field of interpretable machine learning. Winner of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, Cynthia's algorithms are already predicting seizures, aiding crime detection, and powering biological research . We discuss black boxes, Rashomon sets, and what's next for her lab - from cancer detection to interpretable AI-generated music. Can you tell me a bit about your background - what drew you into the field of interpretable machine learning?

Florent Delgrange won the Best Blue Sky Paper Award at AAMAS 2026 for his work Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments . We caught up with him to find out more about his vision for agent learning. What is the topic of your Blue Sky Ideas paper and why is it an interesting area for study? My Blue Sky Ideas paper asks a simple but difficult question: how can an autonomous agent keep learning as its world changes without quietly losing the guarantees that made its behavior trustworthy? Reinforcement learning and formal methods address complementary parts of this problem.

Carleton Gajdusek won the Nobel for his work on a disease in Papua New Guinea. But his biggest experiment was on the children he took back to the U.S. As a boy, Luwi Ikabala moved with Carleton Gajdusek to the U.S. "I am the son of one of the world's greatest scientists," Luwi said. Luwi uses a yellow cooking-oil cannister for a chair. On a good day, he makes enough money from hemming sleeves and mending torn clothes to buy a bag of rice. As he waits for customers, he reads novels, copying passages that he likes into his journal. His favorite author is Fyodor Dostoyevsky. He has kept a journal for the past forty years, giving the entries headings such as "Air of freedom," "As I recall her--'the past,' " "About Weakness," and "Who am I?" Luwi often feels like the "poorest man that ever lived on this planet earth," "a perfect example of a failure." He approaches strangers on the street and asks about their problems, until he sees an opening to "touch them emotionally."

Silicon Valley companies are already working on neurotechnology products that track your brain activity. The next privacy frontier might be the things you only think. Rafael Yuste is in his early sixties and bears a more than passing resemblance to Pablo Picasso--if Picasso had worn glasses and had a trim white goatee. Speaking succinctly and methodically, his accent rich with Spanish inflections, he told me about an experiment he had carried out in his lab at Columbia on the brains of mice, and specifically on that part of the cortex that responds to vision. His mentor had been the Swedish neuroscientist Torsten Wiesel, who won a Nobel Prize for his research into how our visual systems process information. "He discovered by chance that the strongest stimulus is a pattern of high-contrast dark and light bars." He held up one hand and waved his fingers back and forth. "If you imagine my fingers were bars of light surrounded by complete blackness--if I move my fingers in front of your eyes, that fires up your whole visual cortex."

Plus: A new Amazon data center could become the US's most polluting power plant. In 2024, Google DeepMind scientists shared the Nobel Prize in Chemistry for a neural network, AlphaFold, which predicts the structures of proteins. It showed that AI could make groundbreaking scientific discoveries, but AlphaFold may not be the best template for accelerating science. Instead, another approach may hold the key: AI agents. AlphaFold relied on a dataset of roughly 170,000 experimentally validated protein structures that took 53 years and roughly $21 billion worth of experimental work to assemble. Comparable datasets will be difficult or impossible to create in many fields.

AI agents that can model the human process of research will accelerate discoveries in science. Every few decades, someone announces that science has reached its end. In 1903, the revered physicist Albert Michelson wrote that the "facts of physical science have all been discovered." In the 1980s, Stephen Hawking predicted that theoretical physics might be finished by the end of the century. With the explosive arrival of artificial intelligence, the feeling is in the air again--this time accompanied by a Nobel Prize. In 2024, Demis Hassabis and John Jumper of Google DeepMind were awarded part of the Nobel in chemistry for their neural network AlphaFold, which predicts the three-dimensional structures of proteins by learning from thousands of experimentally measured shapes.

When Sir Demis Hassabis said AI had brought the world to a "pivotal moment in human history" last month, he knew another big change was imminent. This shift was closer to home. The Nobel prize-winning head of Google DeepMind, Google's AI unit, announced this week he was relinquishing his day-to-day duties as chief executive and becoming chair. He is also taking on the role of chief scientist at DeepMind's parent, Alphabet. Hassabis and DeepMind have been key players in AI's breakthrough era.

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.

Researchers at the University of Leeds and collaborators from the University of California San Diego won the Best Paper Award at RoboSoft, the leading international conference focused on soft robotics research. Soft robotics is gaining attention in medical applications because compliant machines can interact more safely with delicate objects and complex anatomy. The award-winning paper describes a 1.8 mm soft growing robot that can be steered magnetically, sense its own shape in real time, and operate without internal pressure. These advances could help improve patient outcomes following minimally invasive procedures. We spoke with lead author Benjamin Calmé about the team's work.