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Leading AI models struggle to solve original math problems

Mathematics, like many other scientific endeavors, is increasingly using artificial intelligence. Of course, math is the backbone of AI, but mathematicians are also turning to these tools for tasks like literature searches and checking manuscripts for errors. But how well can AI perform when it comes to solving genuine, high-level research problems?

To date, there is still no widely accepted realistic methodology for assessing AI’s capabilities to solve math at this level. So a group of mathematicians decided to put the machines to the test as they detail in a study available on the arXiv preprint server.

Previous attempts at testing AI have used math contest problems and questions already found in textbooks. What makes this study different is that the questions the programs faced were drawn from mathematicians’ own research. They had never been posted or published online, which means AI couldn’t memorize answers from its training data.

A long-lost Soviet spacecraft: AI could finally solve the mystery of Luna 9’s landing site

Using an advanced machine-learning algorithm, researchers in the UK and Japan have identified several promising candidate locations for the long-lost landing site of the Soviet Luna 9 spacecraft. Publishing their results in npj Space Exploration, the team, led by Lewis Pinault at University College London, hope that their model’s predictions could soon be tested using new observations from India’s Chandrayaan-2 orbiter.

In 1966, the USSR’s Luna 9 mission became the first human-made object to land safely on the moon’s surface and to transmit photographs from another celestial body. Compared with modern missions, the landing was dramatic: shortly before the main spacecraft itself struck the lunar surface, it deployed a 58-cm-wide, roughly 100-kg spherical landing capsule from above, then maneuvered away to crash at a safe distance.

Equipped with inflatable shock absorbers, the capsule bounced several times before coming to rest, stabilizing itself by unfurling four petal-like panels. Although Luna 9 operated for just three days, it transmitted a wealth of valuable data back to Earth, helping to inspire confidence in crewed space exploration, that would see humanity take its first steps on the moon just three years later.

AI decision aids aren’t neutral: Why some users become easier to mislead

Guidance based on artificial intelligence (AI) may be uniquely placed to foster biases in humans, leading to less effective decision making, say researchers, who found that people with a positive view of AI may be at higher risk of being misled by AI tools. The study, titled “Examining Human Reliance on Artificial Intelligence in Decision Making,” is published in Scientific Reports.

Lead author Dr. Sophie Nightingale of Lancaster University said, “Understanding human reliance on AI is critical given controversial reports of AI inaccuracy and bias. Furthermore, the erroneous belief that using technology removes biases may lead to overreliance on AI.”

The research team also included Joe Pearson, formerly of Lancaster University, Itiel Dror from Cognitive Consultants International (CCI-HQ), and Emma Jayes, Georgina Mason, and Grace-Rose Whordley from the Defence Science and Technology Laboratory.

DeepChopper model improves RNA sequencing research by mitigating chimera artifacts

Scientists in the laboratory of Rendong Yang, Ph.D., associate professor of Urology, have developed a new large language model that can interpret transcriptomic data in cancer cell lines more accurately than conventional approaches, as detailed in a recent study published in Nature Communications.

Long-read RNA sequencing technologies have transformed transcriptomics research by detecting complex RNA splicing and gene fusion events that have often been missed by conventional short-read RNA-sequencing methods.

Among these technologies includes nanopore direct RNA sequencing (dRNA-seq), which can sequence full-length RNA molecules directly and produce more accurate analyses of RNA biology. However, previous work suggests this approach may generate chimera artifacts—in which multiple RNA sequences incorrectly join to form a single RNA sequence—and limit the reliability and utility of the data.

Quantum dots reveal entropy production, a key measure of nanoscale energy dissipation

In order to build the computers and devices of tomorrow, we have to understand how they use energy today. That’s harder than it sounds. Memory storage, information processing, and energy use in these technologies involve constant energy flow—systems never settle into thermodynamic balance. To complicate things further, one of the most precise ways to study these processes starts at the smallest scale: the quantum domain.

New Stanford research published in Nature Physics combines theory, experimentation, and machine learning to quantify energy costs during a non-equilibrium process with ultrahigh sensitivity. Researchers used extremely small nanocrystals called quantum dots, which have unique light-emitting properties that arise from quantum effects at the nanoscale.

They measured the entropy production of quantum dots—a quantity that describes how reversible a microscopic process is, and encodes information about memory, information loss, and energy costs. Such measurements can determine the ultimate speed limits for a device or how efficient it can be.

Ordered ‘supercrystal’ could make lasers faster, smaller and more efficient

An advance from Monash University could pave the way for faster, smaller, and more energy-efficient lasers and other light-based technologies. Engineers have developed a new type of perovskite material arranged into an ordered “supercrystal.” In this structure, tiny packets of energy called excitons work together rather than individually, allowing the material to amplify light far more efficiently. The findings, published in Laser & Photonics Reviews, could have applications in communications, sensors, and computing, improving the performance of devices that rely on light, such as sensors in autonomous vehicles, medical imaging, or electronics.

Corresponding author Professor Jacek Jasieniak at Monash Materials Science and Engineering highlighted the potential for faster, more energy-efficient optical devices. “What’s exciting here is that we’re not changing the material itself, but how it’s organized. By assembling nanocrystals into an ordered supercrystal, the excitations created by light can cooperate rather than compete, which allows light to be amplified much more efficiently,” Professor Jasieniak said.

Dr. Manoj Sharma, who led the experimental work at Monash, said their approach revealed new possibilities in nanocrystal assemblies. “By assembling nanocrystals into a highly ordered supercrystal, we show that optical gain is no longer limited by single-particle biexcitons, which are inefficient and prone to energy losses, but instead arises from collective excitonic interactions across the whole structure,” Dr. Sharma said.

Can medical AI lie? Large study maps how LLMs handle health misinformation

Medical artificial intelligence (AI) is often described as a way to make patient care safer by helping clinicians manage information. A new study by the Icahn School of Medicine at Mount Sinai and collaborators confronts a critical vulnerability: when a medical lie enters the system, can AI pass it on as if it were true?

Analyzing more than a million prompts across nine leading language models, the researchers found that these systems can repeat false medical claims when they appear in realistic hospital notes or social-media health discussions.

The findings, published in The Lancet Digital Health, suggest that current safeguards do not reliably distinguish fact from fabrication once a claim is wrapped in familiar clinical or social-media language. The paper is titled “Mapping LLM Susceptibility to Medical Misinformation Across Clinical Notes and Social Media.”

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