quantum assistant

A telescope stares at a star for many nights in a row. Somewhere in the stream of light hides a tiny dip in brightness — an Earth-sized planet briefly blocking part of the star. Such a dip lasts a few hours and repeats once a year or less. Catching it is like hearing a single quiet click in a noisy hall.

At the heart of every neutron star lies a symphony — an intricate interplay of quantum fields and relativistic gravity.

Assessing image quality is a job for a meticulous inspector with a toolbox. The IQA-T1 neural network acts exactly like that: it doesn't just stare at the picture, but applies either a spectroscope or a light meter.

Every week, doctors ask AI assistants millions of questions at the bedside. But these tools are usually tested on exam-style problems, not on what clinicians actually ask in practice. A team of researchers set out to fix that. They collected 620 real questions from physicians across 30 specialties — from cardiology to oncology.

A closed loop: the machine proposes an idea, tests it on people, sees the failures, and spawns a new, more accurate guess. That's how AutoCog works — an automatic cognitive scientist. Previously, in the science of thinking (cognitive science), the hardest part, coming up with a theory, was done only by humans.

Computational studies of material behavior have long suffered from two extremes: either fast but inaccurate models, or accurate but slow physical calculations. Modern programs, trained on data, have nearly resolved this dilemma — now the bottleneck is not computing power, but human routine: setting up simulations and checking results. Paimon works like an autonomous chef.

A laser beam is split into dozens of thin filaments, passes through frosted glass, and reconverges to a single point. This is the workbench where the program took a seat. It received no instructions — only a general goal: figure out how light changes when passing through a scattering medium, and find something new.

A neutron spectrometer is like a blind geologist who taps on rock and guesses what's inside by the sound. It sends a beam of neutrons into a crystal and watches how they scatter. From this pattern, you can deduce how atomic magnets, spins, oscillate. But where exactly to dig and how long to listen used to be decided by a human. Now, the TAS-AI algorithm has taken the helm.

The best AI assistant for scientists scored worse on this exam than a C student on a pop quiz. Just 21% correct answers, and that's the ceiling for the four strongest models. The problems for the new test were written not by programmers but by graduate students: people who deal with real science every day. The test is called COMPOSITE-STEM.

A physicist analyzing particle collisions typically spends weeks on dull work: cleaning noise from data, estimating how many false signals will slip through, double-checking every number. Now that routine can be handed to a program.

Science is like a city where residents pave the streets themselves. Where paths are already worn, new houses go up, while untouched vacant lots go unnoticed — not because building there is impossible, but because no one ever looks.

Science is a vast kitchen where scientist-cooks seek new recipes. Once, they sifted ingredients by hand; now artificial intelligence, a seasoned chef, lends a hand. AI memorizes millions of flavors and suggests surprising pairings: through information search, the algorithm uncovers hidden patterns.

For centuries, three branches of physics (cosmology, particle physics, and nuclear physics) worked like isolated shores.

In 1867, James Clerk Maxwell imagined a demon that sorts gas molecules and thereby violates the second law of thermodynamics. The demon needs to know the speeds of the particles. In the quantum world, a similar task arises: keep entanglement from growing like an avalanche, but you cannot observe the entire system.