The governor’s support signals momentum behind rethinking how the state pays for the cost of natural disasters.

As well as pushing up temperatures, the building El Niño is expected to worsen drought and strain food production in some parts of the world.

The phenomenon, called a pyrocumulonimbus, is so rare in Europe that France’s firefighters federation said it had never before recorded one in the country.

Indonesia faces a dilemma as it attempts a large-scale waste-management reform while also protecting the livelihoods of around 600,000 waste pickers.

Whales are facing a new threat around South Africa: the conflict in the Middle East.

Nature Climate Change, Published online: 28 July 2026; doi:10.1038/s41558-026-02722-w

Traditional resilience planning for extreme weather focuses on reinforcing municipal boundaries and internal infrastructure. Now, a study demonstrates how a city’s socioeconomic and physical ties to external urban networks act as a critical catalyst for intra-city mobility resilience under extreme rainfall.

A new method developed by MIT researchers makes robots better at thinking ahead while they are acting, leading to smoother motions and quicker reactions.

This technique enables the artificial intelligence model that plans a robot’s motion to forecast its future position. The model uses this prediction to seamlessly transition current movements into the next actions.

Many existing methods cause a robot to stop and think about what it needs to do next, leading to slow and jerky motions. By basing its calculations on the future state of the robot, rather than its current position, the MIT method helps robots operate much faster.

Importantly, the technique does not add any computational overhead to the planning process and can be applied to varied robotic hardware.

This new method doubled the speed of robots performing activities like pick-and-place tasks, while significantly reducing lag time between motions. It also boosted the performance of robotic arms in highly dynamic activities, such as playing table tennis and Whack-a-Mole.

The system could be especially useful for robots that perform fast and agile maneuvers in challenging real-world environments, like emergency response or search-and-rescue. It could also allow robots to react more quickly when recovering from mistakes.

“This work sets up a good foundation for efficient, fast, accelerated, and low-cost robotics applications. We look forward to expanding our work into the latest world action models, so it has even stronger capabilities as we keep pushing to make physical AI faster,” says Song Han, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), member of the Research Laboratory of Electronics, and lead author of a paper on this method.

Han is joined on the paper by co-lead authors Jiaming Tang, an MIT EECS graduate student, and Yufei Sun, a student at Tsinghua University; as well as others at Nvidia, the University of California at Berkeley, the University of California at San Diego, and Caltech. The research will be presented at the Intelligent Robots and Systems Conference. 

Forecasting the future 

In state-of-the-art robotics applications, generative AI systems called vision-language-action (VLA) models act as the brain of a robot, planning its next moves and executing those actions. 

A VLA model takes environmental observations from the robot’s camera and instructions about its task, outputs the next few motions as one chunk of actions, then executes those actions on the robotic hardware.

But VLA inference — the real-time procedure during which the model processes visual inputs, reasons about the task, and outputs actions — is computationally demanding, so the robot can experience substantial pauses while planning its next actions. These pauses disrupt the fluidity of its motions and make it slower to react to changes in the environment. 

“Our motivation was to overlap the thinking process with the execution process to make the reaction speed faster,” Tang says.

The MIT researchers developed a new system called VLASH that enables a VLA to predict the future state of the robot and its environment. It uses this information to plan the next set of motions while the robot is completing the current action chunk.

This solves a major hurdle faced by many other methods, which use the current state of the robot to predict its next moves.

“Since the environment will change after the robot moves, if we plan based on stale observations of the current environment, there will be a misalignment that causes very unstable control,” Tang explains.

VLASH avoids this misalignment due to a key insight by the researchers. Although the model doesn’t know exactly what the environment will look like in the future, it does know the robot’s current position and how it will move to perform the actions it is about to take. 

The framework uses this information to predict the state of the robot after it completes its current chunk of actions. It uses that estimation to plan the next motions.

“In this way, we give the robot awareness of its future state,” Tang says.

Augmenting acceleration

On its own, this technique speeds up the robot’s motions by eliminating lag time that usually occurs between action chunks, accelerating reaction speeds more than 30-fold. 

But to make their approach even faster, the MIT researchers generate coarser chunks of actions, so the robot executes a few larger steps that follow the same trajectory. This technique is called action quantization.

While action quantization led to a slight dip in accuracy, it enables a robot to complete the overall task two to three times faster.

However, the researchers found that simply feeding future robot states to the VLA during deployment is not enough to enable accurate and stable control of the robot. 

They developed a training-augmentation method that groups training data in such a way that the VLA learns to use future state information instead of current observations. 

By reusing some training data, this fine-tuning method accelerated training fivefold with no additional computational overhead. 

“Even though there is a very large model working in the background, VLASH lets the robot react and execute its actions very fast, much more like a human would. This could help to make robots for all sorts of dynamic tasks more effective,” Tang says.

When compared with baseline methods in simulation, VLASH consistently performed faster while maintaining the accuracy of robotic maneuvers. The system also outpaced these methods on real hardware in pick-and-place, stacking, and sorting tasks.

For instance, VLASH placed cubes in a box while sorting them by color twice as fast as these methods, while achieving the same 90 percent accuracy as the best baseline. The system can also perform highly dynamic tasks like playing ping-pong and whack-a-mole.

In the future, the researchers want to combine VLASH with more powerful generative AI systems called world models that can predict the robot’s actual environmental observations, in an effort to boost performance and open new applications.

This work is supported, in part, by the MIT-IBM Computing Research Lab, Amazon, the National Science Foundation, and Nvidia.

EFF’s first EFFecting Change livestream was all the way back in July of 2024. Maybe you've caught each stream, or maybe you’ve only caught a few. Or maybe you’re like me and prefer to listen to conversations like these on your daily commute! Either way, if you want to stay on top of these monthly conversations, you can now subscribe to our new podcast feed for EFFecting Change—starting with our conversation on the Right to Repair movement with Adam Savage and iFixit CEO Kyle Wiens:

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And mark your calendar for the next EFFecting Change livestream: Who the Machine Serves. EFF Executive Director Nicole Ozer and Cory Doctorow will be having a conversation on AI, tackling what needs to happen now to ensure AI actually works for everyone, not just those in power. RSVP today!

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After a closet cleanout, what options are there for recyling our old threads? Not many. Apart from bringing used clothes to a donation center, there is no process for recycling textiles like there is for bottles and cans. And, the average American throws out around 81 pounds of clothing each year. That amounts to more than 11 million tons of textiles that end up in the landfill or incinerator. 

But MIT engineers hope to cut down on the growing mountain of textile waste, with a new, recyclable yarn. 

The team has designed a yarn made from a form of plastic that is commonly used in milk bottles and grocery bags. The new yarn, which has a feel similar to traditional sewing thread, can be woven into stretchy, lightweight clothing. The researchers say that at the end of its use, a yarn-spun garment could be melted down and redrawn into new yarn, and then woven into new clothing or even cast into buttons, belt buckles, and other plastic accessories.

To demonstrate the yarn’s recyclability, the researchers spun a spool of yarn, melted the yarn down, and respun it into new yarn, multiple times. They found that even after 10 cycles, the yarn was as strong and flexible as conventional thread. 

They envision the new yarn could be an alternative to elastic spandex-polyester or spandex-nylon yarns, which are spun from a combination of fibers that cannot be recycled together. The team’s new yarn, in contrast, is made from a specific combination of plastic materials that mimics the tough and stretchy properties of spandex yarns, while also being easily recycled. 

“Eighty percent of textiles on the U.S. market currently contain some amount of spandex, which makes them nonrecyclable,” says Svetlana Boriskina, a research scientist in MIT’s Department of Mechanical Engineering. “There’s no widely adopted technology now that recycles textiles into textiles. With our new yarn, we hope to change that.”

Boriskina and her colleagues have published the details of the new yarn in a study published in the journal ACS Materials Letters. MIT co-authors include first author SeongHyeon Kim, Duo Xu, Volodymyr Korolovych, Domingo Flores-Hernandez, Kaniz Moriam, and Daniel Braconnier.

The core of the problem

Spandex is a polyurethane-based synthetic fiber that is springy but not very strong. A thread of an elastic yarn is made from two parts: a spandex-based core, surrounded by a sheath of tough polyester or nylon. The combination of these materials gives elastic yarns their unique stretch and strength. 

But this same material mixture makes elastic yarns nearly impossible to recycle. Yarns would first have to be chemically treated to separate the polyester sheath from the spandex core. The polyester-based sheath material could then be melted down and reused. But there is no way to recycle the yarn as a whole, without chemical separation.

“Even though chemical separation technologies exist, they add extra cost and complexity, and usually require toxic chemicals that are harmful to the environment,” Boriskina says. “That’s why most stretchy garments go to the dump.”

In 2021, Boriskina’s group developed a new type of yarn made from polyethylene. Polyethylene is the most common type of plastic in the world, used to make everything from grocery bags, water bottles, trash bins, and toys to industrial pipes and plastic sheeting. Polyethylene is a thermoplastic, meaning that it can be melted down and remade, and thus recycled. 

And yet, polyethylene had never really been considered as a textile. In their previous work, Boriskina and her colleagues showed they could spin yarn out of polyethylene, which they then wove into various garments. In those experiments, they focused on the yarn’s moisture wicking, stain-resisting, and cooling properties. 

Spaghetti yarn

In their new study, the group aimed to tailor polyethylene yarn to mimic the strength and flexibility of spandex; they also sought to demonstrate the yarn’s recyclability. 

They first looked for formulations of stretchy, polyethylene-based copolymers that resemble a spandex elastic core. Separately, they engineered polyethylene yarns that can act as the sturdier sheath. Looking through the scientific literature and combing through industrial reports, the team evaluated many chemical variations of polyethylene.

“The chemical structure of polyethylene is like Christmas garland — a backbone of carbon, carbon, carbon, and also these dangling ‘decorations’ of hydrogen atoms or short branches with the same structure as a backbone,” Boriskina explains. “How these chains are arranged can change the properties of the whole structure.”

“Polyethylene can give us a wide range of properties, depending on how you make it,” adds first author SeongHyeon Kim.

For the yarn’s core, the team used one polyethylene-based resin that results in a more stretchy fiber. They chose a second, stiffer resin as the basis for the yarn’s sheath. The researchers obtained pellets of each resin from a chemical manufacturer, and then put each type of pellet through a process of fiber fabrication, first pouring them into a hopper, then heating the pellets to about 350 degrees Fahrenheit, past their melting temperature. The melted polyethylene was then drawn through small extruders to make hair-thin fibers. 

“You just melt it in a barrel with a heater, and then you extrude and spin it into fibers,” Kim says. “It’s like a spaghetti machine.”

The team used an industrial yarn spinner to wind the sheath fibers around a core fiber to make the final, elastic yarn. 

Because both the yarn’s core and sheath come from the same chemical family of polyethylene, Boriskina says the materials do not have to be separated before recycling, in contrast to spandex-based elastic yarns. The new yarn can be melted as is, and reformed into new yarn or other plastic products.

“Because they are exactly the same chemistry, they play nicely together,” she says. “That’s what makes this yarn very recyclable.”

As a demonstration, the team twisted an elastic core-sheath yarn, then melted it down and re-spun it, 10 times. Each time, they tested the yarn’s mechanical properties by precisely stretching a thread and measuring the pulling force at which the thread eventually broke. From these tests, they found that the yarn’s recycled versions were just as strong as the original sheath yarn. These recycled yarns can now be used to make new stetchy yarns by twisting them around a newly spun elastic core.

“Now we have something that can be knitted and woven,” Boriskina says. “That is the next stage.”

The team says their new recipe for polyethylene yarn can be scaled up into industrial-sized spools. Just like conventional spandex fibers, it would take kilometers of yarn to weave a single textile. But once woven and used, the team envisions that a polyethylene garment could conceivably be dropped in a recycling bin and sent to a facility to be melted down and respun, enabling a more sustainable, circular fashion and textile economy. 

“Hopefully it will prevent the need for making more and more textile materials, because you can keep recycling a large portion of it,” Boriskina says. 

This work was supported in part by the DEVCOM Soldier Center through the U.S. Army Research Office, the Office of Naval Research Global via Tecnologico de Monterrey, and the MIT Portugal Program.

Yet another Israeli mass surveillance company:

Made by Israeli surveillance company Cognyte, the tech simulates a mobile phone tower, which forces nearby phones to connect to it. That enables cops to keep tabs on any phones in the vicinity ­ whether they’re owned by a suspect in a case or not. Cognyte’s contract with the state of Texas reveals that the simulator, called FalcoNet, can be concealed within the vehicles, hidden in a backpack for on-foot missions or attached to a helicopter. It’s the same technology as the infamous Stingray, one of the original cell-site simulators made by defense giant L3Harris...

The power giant’s move to buy Dominion Energy reflects its plans to become the utility of choice at the epicenter of the AI boom.

Poor countries in Africa and South Asia don't have the resources to expand access to air conditioning as extreme heat increases.

Part of the EU executive’s recent proposal puts controls on free carbon allowances, only releasing 80 percent to companies after they provide a decarbonization plan.

The announcement comes after a group of Ashaninka Indigenous leaders began meetings with federal officials seeking urgent protection for their territory.

The objective is to determine if a newly acquired sample of gold comes from a protected area, meaning it has been illegally mined.

The project is intended to preserve the species’ population and enhance biodiversity.

Nature Climate Change, Published online: 27 July 2026; doi:10.1038/s41558-026-02698-7

The authors integrate tree species richness with satellite-derived photosynthesis proxies to show that richness correlates with greater current levels of photosynthesis and greater increases over time. Projected biodiversity loss by 2050 could lead to cumulative forest photosynthesis loss of 4.4–35.7 PgC.

Nature Climate Change, Published online: 27 July 2026; doi:10.1038/s41558-026-02692-z

Soil carbon stocks are greater than biomass carbon in boreal forests. This study shows that tree canopies thermally protect more carbon in permafrost from thaw than is stored in biomass, highlighting the need to consider carbon impacts of forest disturbance and conservation on an ecosystem scale.

Lower catch this year.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

For years, John Deere had actively made repairing their tractors near-impossible for anyone but itself and the few "authorized" repair shops—regardless of the ability of its customers to actually visit such shops. Now, in a major win for farmers and right to repair advocates, John Deere must soon provide farmers with not just the tools and resources to finally repair their own John Deere equipment, but also access to future updates for said equipment.

In 2025, the Federal Trade Commission (FTC) brought a suit against farm equipment manufacturer John Deere, alleging John Deere used their control over equipment repair tools and resources to limit the ability of farmers and independent repair providers (IRPs) to repair John Deere equipment. Earlier this month, John Deere reached a settlement with the FTC in which they will immediately make available a tranche of repair resources, then continue to make further resources available until the end of the year. Five states joined the FTC in this suit, and over the next 10 years these states will work alongside the FTC to ensure John Deere complies with this settlement. 

It is worth noting there is a second, farmer-initiated antitrust lawsuit against John Deere, also concerning a farmer’s right to repair their own equipment. In April, John Deere agreed to a $99 million settlement in that case, which also includes right to repair provisions.

This fight is just one example of how, as machines become increasingly computerized, companies like John Deere restrict your ability to repair machines behind software subject to legal regimes that don’t just lock down repair, but make unauthorized repair a potential criminal offense. 

John Deere’s market dominance in farm equipment led to an extraordinary power over access to the tools and resources of repair. John Deere actively restricted who had access to repair tools, and monopolized who could do the repair. This revenue stream—and control of it—is built into the business models of a lot of the technology we buy today. It also encourages companies to move away from the kinds of devices that can be easily fixed at home to ones that offer bells and whistles no one wants but makes repair difficult—like app-enabled toasters. 

This whole saga with John Deere has been an exemplar of the greater need for right to repair laws, policy, and enforcement.  There was a time when you bought a tractor and with some know-how and a manual could fix it yourself. It is easy to envision why someone with John Deere farm equipment might find it inconvenient to wait for John Deere approved repairpeople to come and fix any broken equipment. Especially when it meant waiting for days or weeks. Especially if it meant their crop was withering on the vine. This settlement will help ensure this is no longer the case. 

But it’s not just about farm equipment; If you can’t fix it, you don’t own it. While some might feel more willing to agree they “shouldn’t” futz with laptops or smartphone, it still stands that — whether it’s farm equipment, a car, a laptop, or even your phone — if you legally cannot fix it yourself, if you must go hat in hand to an “approved provider,” you are at the mercy of a corporation. It is why EFF continues to support right to repair laws that ensure people truly own what they buy. And it is why EFF continues to fight for exemptions to the law that makes it most difficult to tinker and repair your own devices.