A hotter Atlantic Ocean contributed to unusually powerful downpours, scientists said.

Officials said 15 people are still missing and over 230 have been rescued. More than 5,500 people have been forced to leave their homes.

“At the end of this process, we still believe models tend to underestimate physical risk,” said Norges Bank Investment Management.

In a big win for protesters’ rights, the U.S. Court of Appeals for the Tenth Circuit overturned a lower court’s dismissal of a challenge to sweeping warrants to search a protester’s devices and digital data and a nonprofit’s social media data.

The case, Armendariz v. City of Colorado Springs, arose after a housing protest in 2021, during which Colorado Springs police arrested protesters for obstructing a roadway. After the demonstration, police also obtained warrants to seize and search through the devices and data of Jacqueline Armendariz Unzueta, who they claimed threw a bike at them during the protest. The warrants included a search through all of her photos, videos, emails, text messages, and location data over a two-month period, as well as a time-unlimited search for 26 keywords, including words as broad as “bike,” “assault,” “celebration,” and “right,” that allowed police to comb through years of Armendariz’s private and sensitive data—all supposedly to look for evidence related to the alleged simple assault. Police further obtained a warrant to search the Facebook page of the Chinook Center, the organization that spearheaded the protest, despite the Chinook Center never having been accused of a crime.

The district court dismissed the civil rights lawsuit brought by Armendariz and the Chinook Center, holding that the searches were justified and that, in any case, the officers were entitled to qualified immunity. The plaintiffs, represented by the ACLU of Colorado, appealed. EFF—joined by the Center for Democracy and Technology, the Electronic Privacy Information Center, and the Knight First Amendment Institute at Columbia University—wrote an amicus brief in support of that appeal.

In a 2-1 opinion, the Tenth Circuit reversed the district court’s dismissal of the lawsuit’s Fourth Amendment search and seizure claims. The court painstakingly picked apart each of the three warrants and found them to be overbroad and lacking in particularity as to the scope and duration of the searches. The court further held that in furnishing such facially deficient warrants, the officers violated “clearly established” law and thus were not entitled to qualified immunity. Although the court did not explicitly address the First Amendment concerns raised by the lawsuit, it did note the backdrop against how these searches were carried out, including animus by Colorado Springs police leading up to the housing protest.

It is rare for appellate courts to call into question any search warrants. It’s even rarer for them to deny qualified immunity defenses. The Tenth Circuit’s decision should be celebrated as a big win for protesters and anyone concerned about police immunity for violating people’s constitutional rights. The case is now remanded back to the district court to proceed—and hopefully further vindicate the privacy rights we all have in our devices and digital data.

In a narrow strip of land along the Andes mountain range in central Chile, an Indigenous community has long celebrated the bark of a rare tree for its medicinal properties. Modern science only recently caught up to the tradition, finding the so-called soapbark tree contains potent compounds for boosting the human immune system.

The molecules have since been harnessed to make the world’s first malaria vaccine and to boost the effectiveness of vaccines for everything from shingles to Covid-19 and cancer. Unfortunately, unsustainable harvesting has threatened the existence of the tree species, leading the Chilean government to severely restrict lumbering.

The soapbark tree’s story is not unique. Plants are the foundation of industries such as pharmaceuticals, beauty, agriculture, and forestry, yet around 45 percent of plant species are in danger of going extinct. At the same time, human demand for plant products continues to rise. Ashley Beckwith SM ’18, PhD ’22 believes meeting that demand requires rethinking how plants are grown. Her company, Foray Bioscience, aims to make plant production faster, more adaptable, and less damaging to fragile natural supply chains.

The company is working to make it possible to grow any plant or plant product from single cells using biomanufacturing powered by artificial intelligence. Foray has already developed molecules, materials, and fabricated seeds with various partners, including academic researchers, nurseries, conservationists, and companies.

In one new partnership, Foray is working with the nursery West Coast Chestnut to deploy a more disease-resistant version of the chestnut trees that once filled forests across the eastern U.S. but have since been wiped out. The project is just one example of how AI and plant science can be leveraged to protect the plant populations that bring so much value to humans and the planet.

“Plant systems underpin every aspect of our daily lives, from the air we breathe to the food we eat, the clothes we wear, the homes we live in, and more,” Beckwith says. “But these plant systems are fragile and in decline. We need new strategies to ensure lasting access to the plant products and ecosystems we depend on.”

From human cells to plants

Beckwith focused on biology and materials manufacturing as a master’s student in MIT’s Department of Mechanical Engineering. Her research involved building platforms to enable precision treatments for human diseases. After graduating, she worked on a regenerative, self-sufficient farm that mimicked natural ecosystems, and began thinking about applying her work to address the fragility of plant systems.

Beckwith returned to MIT for her PhD to explore the idea of regenerative plant systems, studying in the lab of Research Scientist Luis Fernando Velásquez-García in the Department of Electrical Engineering and Computer Science.

“To address organ shortages for transplants, scientists aspire to grow kidneys that don’t have to be harvested from a human using tissue engineering,” Beckwith says. “What if we could do something similar for our plant systems?”

Beckwith went on to publish papers showing she could grow wood-like plant material in a lab. By adjusting certain chemicals, the researchers could precisely control properties like stiffness and density.

“I was thinking about how we build products, like wood, from the cell up instead of extracting from the top down,” Beckwith recalls. “It led to some foundational demonstrations that underpin the work we do at Foray today, but it also opened up questions: Where are these new approaches most urgently needed? What would it take to apply these tools where they’re needed, fast?”

Beckwith began exploring the idea of starting a company in 2021, participating in accelerator programs run by the E14 Fund and The Engine — both MIT-affiliated initiatives designed to support breakthrough science ventures. She officially founded Foray in February of 2022 after completing her PhD.

“Our early research showed that we could grow wood-like material directly from plant cells,” she says. “We are now able to grow not just wood without the tree, but also produce harvest-free molecules, materials, and even seeds by steering single cells to develop precisely into the products we need without ever having to grow the whole plant.”

Beckwith describes her lab-grown wood innovation as analogous to Uber if there were no internet — a powerful idea without the digital backbone to scale. To create the data foundation and ecosystem to scale plant innovation, Foray is now building the Pando AI platform to enable rapid discovery and deployment of these novel plant solutions.

“Pando functions like a Google Maps for plant growth,” Beckwith says. “It helps scientists navigate a really complex field of variables and arrive at a research destination efficiently — because to steer a cell to produce a particular product, there might be 50 different variables to tweak. It would take a lifetime to explore each of those, and that’s one reason why plant research is so slow today.”

The “operating system for plant science”

Foray’s team includes experts in plant biology, artificial intelligence, machine learning, computational biology, and process engineering.

“This is a very intersectional problem,” Beckwith says. “One of the most exciting things for me is building this highly capable team that is able to deliver solutions that could never be created in a silo.”

After a year of pilot collaborations with select researchers, Foray is preparing for a broader public launch of its Pando platform early this year.

Over the next several years, Beckwith hopes Foray will serve as an innovation engine for researchers and companies working across agriculture, materials, pharmaceuticals, and conservation. Foray already uses Pando internally to create plant solutions that overcome limitations in natural production.       

“Fabricated seeds are one capability that we’re really excited about,” Beckwith says. “Being able to grow seeds from cells lets you create really timely and scalable seed supplies to address gaps in restoration, or shorten the path to market for new, resilient crop varieties. There’s a lot to be gained by making our plant systems more adaptive.”

“We want to shorten plant development timelines, so solutions can be built in months, not decades,” Beckwith says. “We’re excited to be building tools that represent a step change in the way plant production can be done.”

As Foray’s products scale and more researchers use its platform, the company is hoping to help the plant science industry respond to some of our planet’s most pressing challenges.

“Right now, we’re focused on plants in labs,” Beckwith says. “In five years, we aim to be the operating system for all of plant science, making it possible to build anything from a single plant cell.”

Nature Climate Change, Published online: 27 February 2026; doi:10.1038/s41558-026-02577-1

Regulation of property insurance pricing involves trade-offs that will determine how disaster risks impact households’ budgets. Allowing prices to reflect property-specific risks offers several benefits, but may cause a range of negative unintended consequences associated with declines in coverage.

Nature Climate Change, Published online: 27 February 2026; doi:10.1038/s41558-026-02568-2

Melting beneath floating Antarctic ice shelves is a major driver of ice-shelf mass loss and is projected to increase over the coming century. High-resolution maps of Antarctic basal-melt rates reveal stronger melt within narrow basal channels than previously recognized, making some ice shelves more vulnerable to additional melt channelization.

Nature Climate Change, Published online: 27 February 2026; doi:10.1038/s41558-026-02563-7

A temporary breach of the temperature target, or overshoot, is unavoidable. The authors review the history of how overshoot evolved in mitigation pathways, the magnitude and outcomes of potential physical and socio-economic impacts, and priorities for future model and scenario development.

LLMs are bad at generating passwords:

There are strong noticeable patterns among these 50 passwords that can be seen easily:

• All of the passwords start with a letter, usually uppercase G, almost always followed by the digit 7.
• Character choices are highly uneven ­ for example, L , 9, m, 2, $ and # appeared in all 50 passwords, but 5 and @ only appeared in one password each, and most of the letters in the alphabet never appeared at all.
• There are no repeating characters within any password. Probabilistically, this would be very unlikely if the passwords were truly random ­ but Claude preferred to avoid repeating characters, possibly because it “looks like it’s less random”. ...

American officials are drafting a diplomatic cable that warns dozens of countries against adopting a climate fee on the shipping industry.

An industry group told federal officials that losing the National Center for Atmospheric Research would weaken insurance “stability and affordability.”

The research focused on Seattle, a city with relatively clean air but that sees occasional spikes in smoky days due to Western wildfires.

Each chamber of the Legislature has advanced proposals that would provide legal protections for farmers and ethanol producers.

Companies that have to cover their emissions under California's cap-and-trade system exhibited low demand in last week's sale.

The Legislative Analyst’s Office warned that the plan could disrupt transportation funding with little emissions benefit.

The bill is the latest move from Florida Republicans to scrap climate change policy.

Officials said that the food security situation is deteriorating because of water shortages, insecurity, conflict and historically low levels of humanitarian assistance.

The Net Zero Asset Managers initiative suspended its operations in January 2025, announcing at the time that it would conduct a review to ensure it remained “fit for purpose.”

In industrial plants around the world, tiny bubbles cause big problems. Bubbles clog filters, disrupt chemical reactions, reduce throughput during biomanufacturing, and can even cause overheating in electronics and nuclear power plants.

MIT Professor Kripa Varanasi has long studied methods to reduce bubble disruption. In a new study, Varanasi, along with PhD candidate Bert Vandereydt and former postdoc Saurabh Nath, have uncovered the physics behind a promising type of debubbling membrane material that is “aerophilic” — Greek for “air-loving.” The material can be used in systems of all types, allowing anyone to optimize their machine’s performance by breaking free from bubble-borne disruptions.

“We have figured out the structure of these bubble-attracting membrane materials to allow gas to evacuate in the fastest possible manner,” says Varanasi, the senior author of the study. “Think of trying to push honey through a coffee strainer: It’s not going to go through easily, whereas water will move through, and gas will move through even more easily. But even gas will reach a throughput limit, which depends on the properties of the gas and the liquid involved. By uncovering those limits, our research allows engineers to build better membranes for their systems.”

In the paper, which appears in the journal PNAS this week, the researchers distill their findings into a graph that allows anyone to plot a few characteristics of their system — like the viscosity of their gas and the surrounding liquid — and find the best membrane to make bubble removal near-instantaneous. Using their approach, the research team demonstrated a 1,000-fold acceleration in bubble removal in a bioreactor that’s used in the pharmaceutical industry, food and beverage manufacturing, cosmetics, chemical production, and more.

The researchers say the membranes, which repel water, could be used to improve the throughput of a wide range of advanced systems whose operation has been plagued to date by bubbles.

Better bubble breakers

Companies today try everything to burst bubbles. They deploy foam breakers that physically shear them, chemicals that act as antifoaming agents, even ultrasound. Such approaches have drawbacks in tightly controlled environments like bioreactors, where chemical defoamers can be toxic to cells, while mechanical agitation can damage delicate biological materials. Similar limitations apply to other industries where contamination or physical disturbance is unacceptable. As a result, many applications that cannot tolerate chemical defoamers or mechanical intervention remain fundamentally bottlenecked by foam formation.

“Biomanufacturing has really taken off in the last 10 years,” Vandereydt says. “We’re making a lot more out of biologic systems like cells and bacteria, and our reactors have increased in throughput from 5 million cells per millimeter of solution to 100 million cells per millimeter. However, the bubble evacuation and defoaming haven’t kept up — it’s becoming a significant rate-limiting step.”

To better understand the interaction between aerophilic membranes and bubbles, the MIT researchers used MIT.nano facilities to create a series of tiny porous silicon membranes with holes ranging in size from 10 microns to 200 microns. They coated the membranes with hydrophobic silica nanoparticles.

Placing them on the surface of different liquids, the researchers released single bubbles with varying viscosity and recorded the interaction using high-speed imaging as each collided with the membranes.

“We started by trying to take a very complicated system, like foam being generated in a bioreactor, and study it in the simplest form to understand what’s happening,” Vandereydt says.

At first, the bigger the holes, the faster the bubbles disappeared. The researchers also changed the bubble gas from air to hydrogen, which has half the viscosity, and found the speed of bubble destruction doubled.

But after about a 1,000-fold acceleration in bubble destruction, the researchers hit a wall no matter how big the membrane holes were. They had run up against a different physical limit to investigate.

The researchers then tried changing the viscosity of their liquid, from water to something closer to honey. They found viscosity only plays a role in the speed of bubble destruction when the liquid is 200 times the viscosity of liquid. Further experiments revealed the biggest factor for slowing bubble evacuation was inertial resistance in the liquid.

“Through experimentation, we showed there are three different limits [to the speed of bubble destruction],” Vandereydt says. “There is the viscous limit of the gas in a low-viscosity, low-permeability setup. Then there’s the viscous resistance of the liquid in the high-permeability, high-viscosity regime. Then we have the inertial limit of the liquid.”

The team used a bioreactor to experimentally validate their findings and charted them in a map that engineers can use to enter the characteristics of their system and find both the best membrane for their situation and the biggest factor slowing bubble evacuation.

The science of bubbles

The research should be useful for anyone trying to accelerate the destruction of bubbles in their industrial device, but it also improves our understanding of the physics underpinning bubble dynamics.

“We have identified three different throughput limits, and the physics behind those limits, and we have reduced it to very simple laws,” Nath explains. “How fast you can go is first dictated between surface tension and inertia. But you may also hit a different limit, where the pores are extremely small, so the gas finds it difficult to move through them. In that case, the viscosity of the gas is meaningful. But you may also have a bubble which was originally in something like honey, which means it’s not enough the gas is moving, the liquid also must refill the space behind it. No matter what your conditions are, you will be switching between these three limits.”

Varanasi says health care companies, chemical manufacturers, and even breweries have expressed interest in the work. His team plans to commercially develop the membranes for industrial use.

“These physical insights allowed us to design membranes that, quite surprisingly, evacuate bubbles even faster than a free liquid-gas interface,” says Varanasi.

The researchers’ design map could also be used to model natural systems and even liquid-liquid systems, which could be used to create membranes that remove oil spills from water or help efficiently extract hydrogen from water-splitting electrodes. Ultimately the biggest beneficiaries of the findings will be companies grappling with bubbles.

“Though small, bubbles quietly dictate the performance limits of many advanced technologies,” says Varanasi. “Our results provide a way to eliminate that bottleneck and unlock entirely new levels of performance across industries. These membranes can be readily retrofitted into existing systems, and our framework allows them to be rapidly designed and optimized for specific applications. We’re excited to work with industry to translate these insights into impact.”

The work was supported, in part, by MIT Lincoln Laboratory and used MIT.nano facilities.

Reasoning large language models (LLMs) are designed to solve complex problems by breaking them down into a series of smaller steps. These powerful models are particularly good at challenging tasks like advanced programming and multistep planning.

But developing reasoning models demands an enormous amount of computation and energy due to inefficiencies in the training process. While a few of the high-power processors continuously work through complicated queries, others in the group sit idle.

Researchers from MIT and elsewhere found a way to use this computational downtime to efficiently accelerate reasoning-model training.

Their new method automatically trains a smaller, faster model to predict the outputs of the larger reasoning LLM, which the larger model verifies. This reduces the amount of work the reasoning model must do, accelerating the training process.

The key to this system is its ability to train and deploy the smaller model adaptively, so it kicks in only when some processors are idle. By leveraging computational resources that would otherwise have been wasted, it accelerates training without incurring additional overhead.

When tested on multiple reasoning LLMs, the method doubled the training speed while preserving accuracy. This could reduce the cost and increase the energy efficiency of developing advanced LLMs for applications such as forecasting financial trends or detecting risks in power grids.

“People want models that can handle more complex tasks. But if that is the goal of model development, then we need to prioritize efficiency. We found a lossless solution to this problem and then developed a full-stack system that can deliver quite dramatic speedups in practice,” says Qinghao Hu, an MIT postdoc and co-lead author of a paper on this technique.

He is joined on the paper by co-lead author Shang Yang, an electrical engineering and computer science (EECS) graduate student; Junxian Guo, an EECS graduate student; senior author Song Han, an associate professor in EECS, member of the Research Laboratory of Electronics and a distinguished scientist of NVIDIA; as well as others at NVIDIA, ETH Zurich, the MIT-IBM Watson AI Lab, and the University of Massachusetts at Amherst. The research will be presented at the ACM International Conference on Architectural Support for Programming Languages and Operating Systems.

Training bottleneck

Developers want reasoning LLMs to identify and correct mistakes in their critical thinking process. This capability allows them to ace complicated queries that would trip up a standard LLM.

To teach them this skill, developers train reasoning LLMs using a technique called reinforcement learning (RL). The model generates multiple potential answers to a query, receives a reward for the best candidate, and is updated based on the top answer. These steps repeat thousands of times as the model learns.

But the researchers found that the process of generating multiple answers, called rollout, can consume as much as 85 percent of the execution time needed for RL training.

“Updating the model — which is the actual ‘training’ part — consumes very little time by comparison,” Hu says.

This bottleneck occurs in standard RL algorithms because all processors in the training group must finish their responses before they can move on to the next step. Because some processors might be working on very long responses, others that generated shorter responses wait for them to finish.

“Our goal was to turn this idle time into speedup without any wasted costs,” Hu adds.

They sought to use an existing technique, called speculative decoding, to speed things up. Speculative decoding involves training a smaller model called a drafter to rapidly guess the future outputs of the larger model.

The larger model verifies the drafter’s guesses, and the responses it accepts are used for training.

Because the larger model can verify all the drafter’s guesses at once, rather than generating each output sequentially, it accelerates the process.

An adaptive solution

But in speculative decoding, the drafter model is typically trained only once and remains static. This makes the technique infeasible for reinforcement learning, since the reasoning model is updated thousands of times during training.

A static drafter would quickly become stale and useless after a few steps.

To overcome this problem, the researchers created a flexible system known as “Taming the Long Tail,” or TLT.

The first part of TLT is an adaptive drafter trainer, which uses free time on idle processors to train the drafter model on the fly, keeping it well-aligned with the target model without using extra computational resources.

The second component, an adaptive rollout engine, manages speculative decoding to automatically select the optimal strategy for each new batch of inputs. This mechanism changes the speculative decoding configuration based on the training workload features, such as the number of inputs processed by the draft model and the number of inputs accepted by the target model during verification.

In addition, the researchers designed the draft model to be lightweight so it can be trained quickly. TLT reuses some components of the reasoning model training process to train the drafter, leading to extra gains in acceleration.

“As soon as some processors finish their short queries and become idle, we immediately switch them to do draft model training using the same data they are using for the rollout process. The key mechanism is our adaptive speculative decoding — these gains wouldn’t be possible without it,” Hu says.

They tested TLT across multiple reasoning LLMs that were trained using real-world datasets. The system accelerated training between 70 and 210 percent while preserving the accuracy of each model.

As an added bonus, the small drafter model could readily be utilized for efficient deployment as a free byproduct.

In the future, the researchers want to integrate TLT into more types of training and inference frameworks and find new reinforcement learning applications that could be accelerated using this approach.

“As reasoning continues to become the major workload driving the demand for inference, Qinghao’s TLT is great work to cope with the computation bottleneck of training these reasoning models. I think this method will be very helpful in the context of efficient AI computing,” Han says.

This work is funded by the MIT-IBM Watson AI Lab, the MIT AI Hardware Program, the MIT Amazon Science Hub, Hyundai Motor Company, and the National Science Foundation.