A team of graduate students at Shanghai Jiao Tong University recently asked 14 different AI systems a simple question: given two scientific papers, can you tell which one built on the other? The best AI got it right 27.3% of the time. A coin flip would have done about as well.
Gregory Bassen had a simple question: could a large language model write a recipe for making a ceramic oxide that actually works in a real lab? Not in theory. Not in simulation. In a furnace, with real powders, producing a material you could hold in your hand.
In 2023, the world spent over $200 billion on antibody drugs — treatments like Keytruda for cancer and Humira for autoimmune disease. But finding a single antibody that binds well enough to become a drug is like searching for a specific grain of sand on a beach. A typical drug discovery campaign screens thousands of candidates, each one requiring expensive lab tests to measure how tightly it grabs its target. Most fail.
The European Centre for Medium-Range Weather Forecasts has spent decades running the world’s most sophisticated weather simulations on some of the planet’s largest supercomputers. Their model, the Integrated Forecasting System, crunches through the laws of physics hour by hour to predict what the atmosphere will do next. Last week, ECMWF announced that a machine learning model using 200 times less energy can now match it. In the weeks-ahead range that matters most for disaster planning, it can beat it.
That model is called AIFS-SUBS, and it is ECMWF’s first AI system designed specifically for sub-seasonal forecasting: the awkward middle ground between next week’s weather and next season’s climate. Meteorologists call this the “predictability desert.”
For years, decoding brain activity meant picking one direction: you could either read what someone was seeing from their fMRI scan, or you could predict brain activity from an image. Never both in the same system. A team of researchers from Tianjin University, Shanghai Artificial Intelligence Laboratory, and the Chinese University of Hong Kong just tore down that wall. Their model, BrainJanus, reads brain signals and generates them, all within a single framework.
Every time you ask ChatGPT or Gemini a hard question, something remarkable happens behind the scenes. The model doesn’t just blurt out an answer — it thinks. It writes out a stream-of-consciousness chain of reasoning, step by step, in plain English, before delivering its final response. This isn’t just a quirk; it’s a safety feature. When an AI writes down its thoughts in human language, researchers can read them. They can spot signs of deception, catch flawed logic, and — if the model ever starts plotting something dangerous — hopefully intercept it before it’s too late.
Imagine being handed a box containing more objects than there are stars in the observable universe — and being told that somewhere inside it lies a material that could make batteries last ten times longer, or a molecule that kills antibiotic-resistant bacteria. You get to test a few hundred items before your budget runs out. Good luck.
That, roughly speaking, is the problem facing chemists who search for new molecules. The space of synthesizable chemical compounds is estimated to exceed 10⁶⁰ candidates — a number so large it mocks the very idea of systematic search. For decades, discovering a useful new molecule has been a mixture of chemical intuition, educated guesswork, and sheer persistence. Now, a team from Los Alamos National Laboratory and Georgia Tech has built an AI system that learns how to search — and it’s dramatically better than anything we’ve had before.
You’d think that adding a citation to an AI’s answer would make it more trustworthy. A link to a scientific paper. A reference to a legal precedent. A footnote pointing to a medical journal. Surely the AI has checked its sources, right?
Wrong. A landmark new study accepted at ICML 2026 — one of the world’s most prestigious AI conferences — reveals something deeply counterintuitive: citations make large language models more likely to hallucinate, not less.
There’s a quiet unease that comes with using AI these days. Not the fear that it will take your job or go rogue — something subtler. It’s the sense that after spending hours with these systems, you start to think a little differently. Your questions become more like prompts. Your internal monologue sounds more like a chatbot. You catch yourself writing in bullet points.
A new paper from researchers across Union College, the Catholic University of the Sacred Heart, and Lenovo asks whether this feeling is more than just a feeling. They call it cognitive colonization.