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Iron, sulfur and purple light unlock greener carbon-carbon bond formation

Much of organic chemistry, including the type used to create and alter medicinal drugs, relies on modifying carbon bonds. Rice University’s Julian West has been developing a less expensive, faster and more environmentally friendly way to catalyze these reactions, with a breakthrough published in the journal Nature Catalysis.

“Our work is essentially that of molecular architects. In my lab, we are particularly interested in using inexpensive and easily available building blocks to build and modify organic compounds,” said West, an assistant professor of chemistry and corresponding author of the study.

“Now, we’ve adapted our cheap and environmentally friendly method to precisely and specifically bind a carbon to other carbons, an essential reaction for making organic molecules.”

Quadrillion-parameter transformers and how to train them

“Humans are ~1,000,000x more sample efficient than LLMs, and there are no good explanations.”

“We’ve found that a big part of the answer is that LLMs are *massively* underparameterized by many orders of magnitude. The brain has 100T synapses learning from ~200M tokens of language. Chinchilla prescribes a 1T-parameter model on 20T tokens. That’s 7 OOMs apart in per token.”

“After a lot of painstaking work, we present new data-constrained scaling laws and learning algorithms to scale to (extrapolated) quadrillion-parameter transformers.”

The current consensus in frontier pre-training is clear: high-quality human text is finite, but compute scaling is not slowing down. The key question for AI leaders is: How do we scale compute when data is constrained?

A breakthrough paper from Q Labs (*Baron et al., Oct 2026• provides a rigorous roadmap: Quadrillion-parameter architectures, aggressive weight decay, and first-class ensembling.

Here is the breakdown of the operational shift every AI research and infrastructure team should track:

1. The Parameter-to-Token Shift.

Continue reading “Quadrillion-parameter transformers and how to train them” | >

Quantum models predict lung cancer using limited training data in preliminary tests

Developing reliable machine learning models for health care can be challenging when only small datasets are available. This challenge motivates research into whether quantum computing approaches can learn useful patterns from limited symptom and risk-factor data.

A research team, led by Arslan Munir, Ph.D., professor in Florida Atlantic University’s Department of Electrical Engineering and Computer Science within the College of Engineering and Computer Science, and director of the Intelligent Systems, Computer Architecture, Analytics, and Security Laboratory, evaluated quantum machine learning approaches in simulation and on quantum hardware using symptom and risk-factor data for lung cancer prediction under limited training data.

The study, published in Discover Quantum Science, “Inferring lung cancer under limited learning data through gate-based and annealing-based quantum techniques,” provides a common framework for evaluating different quantum machine learning methods under the same data preparation and testing conditions.

MIT engineers create nanoparticles for delivering personalized cancer vaccines

A nanoscale particle with a unique structure, developed at MIT, holds potential as a customizable delivery vehicle for personalized cancer vaccines.

The backbone of the vaccine is a modified, harmless bacteriophage called M13, which forms a nanofiber that can be coated with cancer antigens. By precisely controlling the density of those cancer proteins, the researchers found that they could create a vaccine that prevents the development of colon tumors in mice.

This type of delivery particle could be adapted to carry many different antigens, allowing for the creation of personalized vaccines to treat individual patients’ tumors, the researchers say.

Humanoid AI connects walking and object handling without demonstrations of complete task sequences

A humanoid AI system has been developed that learns jointly from separately collected locomotion and object-manipulation data to perform multiple stages of a task in sequence.

A research team led by professor Songhwai Oh of the Department of Electrical and Computer Engineering at Seoul National University College of Engineering has developed RISE (Responsibility-Induced Specialized Experts), a vision-language-action (VLA) framework that learns jointly from locomotion and object-manipulation demonstrations collected independently and enables a humanoid robot to perform multistep tasks sequentially.

The team connected locomotion and object manipulation without requiring additional demonstrations of multistep tasks involving transitions between the two behaviors, and validated the framework in both simulation and experiments using a real humanoid robot, the Unitree G1.

Flexible connectors enable new designer materials built from nanoparticles

Creating better-performing materials comes down to having more control over their structure at the smallest possible scale. One particularly effective way to accomplish that is using nanoparticles, which have highly customizable shapes and sizes and have already shown promise in applications across energy, medicine and electronics.

But the process of turning nanoparticles into useful new materials has been hampered by the difficulty of controlling the number and direction of the links between each particle and its neighbors. Researchers have tried changing the shape of particles or precisely aligning the rigid connector chains between them, but both of those methods limit the material structures nanoparticles are able to form and are difficult to scale.

A team including researchers from MIT is taking a different approach. The researchers developed a tunable way to use superflexible connector molecules that regulate the packing of nanoparticle networks by intentionally deforming based on their surrounding environment. In a paper published in Nature Materials, the researchers showed how connector flexibility and electric charges can be used to tune the network’s structure, and they used the technique to create new kinds of nanoparticle-based materials.

Scientists reverse liver cell aging and cut Alzheimer’s-linked protein in half

A surprising discovery suggests that rejuvenating the liver could help protect against Alzheimer’s disease and heart disease. USC researchers found that an experimental drug restored aging liver cells in rats, cutting levels of an Alzheimer’s-linked protein in half and bringing bad cholesterol down to youthful levels. The treatment also revived a previously unknown communication system that helps liver cells remove harmful substances from the bloodstream.

MIT uses flexible nanoparticle links to create stronger new materials

MIT and University of Delaware researchers have disproved a twenty-year rule in nanotechnology by demonstrating that nanoparticle assembly does not require rigid molecular connectors.

Scientists previously believed that flexible connectors caused structures to collapse into disorder. But the team showed that super-flexible, charged polymer connectors let nanoparticles spontaneously reorganize and form customizable, open material structures. This flexible assembly method makes it easier to scale designer materials for applications in medicine, energy, and photonics.