Temperatures will be 15 to 25 degrees Fahrenheit warmer than normal in many areas, including at night, officials said.

The boats are commonly made of carbon fiber, fiberglass and PVC foam, which take a lot of energy to produce in processes that emit carbon pollution.

Officials warn a deal with South American nations could undercut British farmers.

Europe is the world’s fastest-warming continent, with temperatures increasing twice as fast as the global average since the 1980s, according to officials.

With the exploding popularity of generative artificial intelligence, many open-source models are now available online for anyone to adapt for their task, such as generating product renderings in a certain artistic style. 

But these models also find their way into the hands of nefarious actors who may optimize them to produce illegal content, like hate speech or child sexual abuse material (CSAM). This is a growing problem — the National Center for Missing and Exploited Children received more than 1.5 million reports of AI-generated CSAM in 2025, an increase from 67,000 in 2024.

Engineers usually test AI for harmful capabilities by prompting the model and inspecting its outputs, but this is impossible for CSAM, since it is illegal in the U.S to generate such content, regardless of intent.

To avoid this dilemma and improve AI safety, a team of MIT scientists, led by graduate student Vinith Suriyakumar and associate professors Ashia Wilson and Marzyeh Ghassemi, joined forces with researchers from Thorn to develop a new auditing approach that determines whether a model can produce CSAM, without prompting it. Thorn is a child safety nonprofit whose mission is to transform how children are protected from sexual abuse and exploitation in the digital age.

Their technique examines how the inner workings of a model have been adapted, but it never generates an output. By examining hidden representations, it can reliably infer whether a model has been specialized to produce harmful imagery.

When tested, the auditing procedure identified model variations that had been specialized to generate CSAM with 100 percent accuracy. A hosting platform could use this technique to flag unsafe models and quickly remove them or prevent them from being uploaded in the first place.

“This unlocks a new avenue for platforms that host open-source models and for law enforcement to actually test whether a model is capable of generating CSAM. Before, we had no way of measuring this. It was a huge blind spot that some people were taking advantage of. Now, we can address an AI safety problem that is having severe negative impacts,” says Vinith Suriyakumar, an MIT electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique.

Suriyakamur and Wilson, the Lister Borthers Career Develop Professor in EECS and a principal investigator in the Laboratory for Information and Decision Systems (LIDS), are joined on the paper by Lena Stempfle, an MIT postdoc; Ghassemi, an associate professor in EECS and a member of the Institute of Medical Engineering Sciences (IMES) and LIDS; and others at Boston University and Thorn. The paper was be presented as a spotlight at the “Trustworthy AI for Good” workshop at the International Conference on Machine Learning.

Auditing adaptations

Recent techniques have made it easier for users to specialize a generative AI model for their task through a process known as fine-tuning. 

Rather than retraining the entire model on a task-specific dataset, individuals can utilize an algorithm called low-rank adaptation (LoRA) to specialize the model in a more efficient manner.

This has led to a wave of new generative AI model variants for a variety of purposes, like producing watercolor images that mimic an artistic movement. But it has also enabled malicious actors to create models that can generate high-quality CSAM and other harmful imagery.

To audit a model, engineers typically prompt it for harmful content and check its outputs, but this manual auditing procedure is not scalable. In addition, repeatedly generating heinous images can have negative psychological impacts on human evaluators. 

This evaluation method quickly falls apart when testing CSAM, which is illegal to generate for any purpose in the U.S. and many other international jurisdictions.

“We are in this very difficult situation where, based on the law itself, we cannot use the de facto means of evaluation. We had to throw out the entire toolkit and take a different approach,” Suriyakumar says.

After learning about this conundrum, the researchers joined forces with Thorn, to address this issue.

A nongenerative solution

Instead of focusing on outputs, the researchers targeted the modifications a LoRA algorithm makes during fine-tuning. 

Their technique probes these modifications, called LoRA adaptors, to determine whether a model has been specialized for a harmful capability, without generating an output.

Using a technique called Gaussian probing, the researchers feed the model a set of random data points and analyze how it manipulates those data within its multilayer internal structure. 

“We never run the model all the way to the end or prompt the model, so we never generate images,” Suriyakumar explains.

The researchers capture those modifications at multiple time points within the model’s inner structure and average them to summarize how the LoRA adaptor changed the model’s computation. They found these responses to be a strong signal of how a model had been specialized.

They tested their method on variations of three types of models, comparing the results to ground-truth data from LoRA adaptors known for generating CSAM, other harmful images, and safe content. 

Their method was 100 percent accurate in identifying models that had been adapted to generate CSAM. 

“There is a huge bucket of child safety concerns with AI, and these are real concerns that need to be addressed. A lot of children are being harmed by AI deepfakes. We’ve shown that Gaussian probing can be a very useful tool, and we hope the research community really pours more attention into this problem,” Wilson says.

Importantly, their technique is scalable and would be relatively inexpensive to implement. Since thousands of model variations are published online every month, scalability is key to help auditors remove harmful adaptations before they are widely distributed.

Gaussian probing is also more robust than some other auditing techniques, since a nefarious actor would need to carefully alter the inner workings of the base model to avoid detection.

In the future, the researchers want to evaluate their technique on a larger set of model variations and explore whether Gaussian probing can detect harmful capabilities in base models before they are adapted.

“Now we have a technological approach to partially address this concern. So much effort was poured into this collaboration, which enabled us to tackle a really hard problem that is harming so many children, nationally and around the world. Hopefully, we can have a transformative impact in this area,” Ghassemi says.

This work was supported, in part, by the Bridgewater AIA Labs Research Fellowship.

Infrared cameras can be used to spot useful information that our eyes can’t see, such as gases escaping from a pipeline, chemicals in the atmosphere, or heat leaking from a building. But sensing infrared light in sophisticated ways still requires expensive and bulky systems.

Now MIT researchers have created a chip-based optical device that can dynamically control incoming infrared light, to act as a tunable lens that gathers additional information for infrared cameras. Each microscopic pixel of the device’s lens can control infrared light independently, allowing it to change its focus and help cameras detect different signals without moving parts.

The system is described in a paper published in Nature Communications. The researchers also explain how they built a lab-scale demonstration using mostly conventional manufacturing processes in a semiconductor chip factory, suggesting the approach could be implemented at industrial scales.

The technology could enable compact, tunable infrared cameras for more dynamic thermal imaging, chemical sensing, pollution monitoring, and even new kinds of optical computing.

“This could give us more information as we study space, or help with environmental protections where you want to monitor for specific compounds in the atmosphere,” explains first author Cosmin-Constantin Popescu PhD ’25. “Thermal imaging is another application, and you can think of military applications where night vision goggles are currently being used. Basically, a lot of organic molecules absorb in the mid-infrared wavelength, and you could use this system to detect them.”

Joining Popescu on the paper are MIT PhD students Maarten Robbert Anton Peters and Khoi Phuong Dao; Dynasil company scientists Oleg Maksimov and Harish Bhandari; University of Central Florida PhD candidate Kathleen Richardson and scientist Rashi Sharma; University of Washington Professor Arka Majumdar; Korea Advanced Institute of Science and Technology Associate Professor Hyun Jung Kim; MIT postdoc Rui Chen; Luigi Ranno PhD ’25; Brian Mills ’20, PhD ’26; Draper Laboratory scientist Dennis Calahan; MIT principal investigator Tian Gu; and Juejun Hu, MIT’s John F. Elliott Professor of Materials Science and Engineering.

Chip-based lenses

In recent years, researchers have developed ways to dynamically control light by etching tiny, precise patterns on transparent materials known as “metasurfaces,” which could enable more compact, programmable cameras and other advanced optical devices.

Hu’s research group at MIT has experimented with a class of metasurfaces that shift from solid to liquid after heat is applied. The phase changes can be harnessed to control how the materials interact with light. In 2021, Hu and collaborators created a miniature lens that could adjust its focus to different depths through such phase changes.

The device worked reliably, but it could only adjust focus all at once across the entire material, which is how most metasurfaces work. For their new study the researchers wanted to build on that approach to control light independently at each microscopic pixel of the material.

“Most active metasurfaces trying to do single-pixel tuning need wires going to every pixel, and how you route the wires becomes a big issue,” Hu explains. “The best approach so far has been one-dimensional pixel control with a bunch of wires.”

The researchers also wanted to create a system that worked with the mid-infrared wavelength of light, which the human eye can’t see but is useful for detecting heat signatures and molecules including methane and propane. Mid-infrared detection devices are already used to detect gas leaks and study Earth’s atmosphere, and for a number of defense and aerospace applications.

To build their system, the researchers adapted an approach commonly used in displays in which two layers of neatly packed copper wires are placed on top of each other perpendicularly. Below the wires is a layer of doped silicon that generates heat at the cross points of the wires and sits on top of the phase-change material. The silicon’s heat is used to switch each pixel of the material between crystalline and amorphous structures, which changes how the material interacts with the infrared light coming in. The silicon also includes a diode selector, which helps prevent unintended currents from leaking through neighboring pixels.

“We did some calculations showing this architecture allows us to scale to potentially millions of pixels without having any issues with the [unintended] currents,” Hu says. “The key innovation is this crossbar architecture, which creates a scalable way to increase the pixel-level switching of metasurfaces. We didn’t invent this architecture — it’s used in displays — but it’s the first time anyone’s used it for active phase-change metasurfaces to show you can get pixel-level control. People have been working toward two-dimensional pixel-level control for a long time, and it’s the first time anyone’s implemented it.”

The researchers worked with equipment in MIT.nano and with a factory that manufactures semiconductor chips, ultimately creating a two-dimensional system that featured a 6-by-6 metasurface pixel array. They tested their system and found it could switch on and off reliably.

“We found this mesh architecture to be very resilient,” Popescu says. “You don’t want these materials to switch once and not work anymore. You want it to switch a large number of times: maybe tens of thousands of times or more.”

Scaling up

The researchers say integrating part of their system’s design into existing semiconductor manufacturing should help it move beyond a research prototype.

“As you want to scale up, you need something that’s part of a consistent process, and that’s why chip foundry manufacturing becomes so important,” Hu says. “Working with a semiconductor foundry with well-defined process control is very powerful. It also allows you to implement each of the components into a single efficient process.”

The researchers are working to add more pixels to their array and develop more robust versions of their system so that it can start capturing more infrared information.

“In lots of cases when you’re taking images, you have prior knowledge of what you’re looking for,” Hu says. “You might be looking for a human in a dark room, or some specific features in an image, like a tree, and that prior information can be useful because now you can configure this system to specifically highlight those features.”

Hu also notes that researchers have used metasurfaces to emulate computational neural networks that power AI systems, though he notes that applications could be farther away from taking hold.

“This could enable more effective optical computing, where metasurfaces are used to encode network weights in neural networks,” Hu explains. “When light passes through the material, it interacts with the metasurface, and that information gets encoded in such a way that you can infer computational results. Researchers have already used this approach to emulate very complex neural networks.”

The work was supported, in part, by the U.S. Air Force, the U.S. National Science Foundation, the National Research Foundation of Korea, and the Draper Scholar Program.

Nature Climate Change, Published online: 13 July 2026; doi:10.1038/s41558-026-02706-w

Ecology is not at risk of a methodological balance crisis

In a rare combined cybersecurity/squid post, a twenty-nine-year-old squid proxy bug can leak HTTP requests.

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.

A new study led by MIT researchers could drive the development of more energy-efficient digital displays — such as flat-screen TVs, augmented and virtual reality headsets, smartphone screens, medical imaging devices, and even large-area ambient lighting surfaces — that also generate richer, brighter colors.

The MIT scientists, in collaboration with researchers at Samsung, studied the microscopic changes that occur inside LEDs that utilize electrically excited quantum dots, which are precisely shaped nanoscale semiconductor particles that emit extremely pure colored light. 

Quantum dots are currently used in some of the computer and television displays with the best picture quality available. The efficiency of these displays could be further improved, and their manufacturing process further simplified, if the quantum dots could be electrically excited, as was first demonstrated in the quantum dot LED (QD-LED) structures over 20 years ago. 

But limitations on the operating lifespans of these QD-LEDs have prevented their widespread use in commercial applications.

The new study shows how encapsulating QD-LEDs in an acrylate-based resin can extend their lifespan by minimizing the physical degradation that would otherwise occur during QD-LED operation. 

The researchers demonstrated that encapsulating QD-LEDs with a resin layer using a simple, scalable process boosts stability and performance. In some devices, resin encapsulation enabled a 5,000-fold lifespan improvement. Importantly, their study reveals the fundamental reasons resin encapsulation is effective.

“The insights into how and why quantum dot LEDs get modified during their operation open the possibility of fixing everything that holds back commercialization of QD-LED displays. This technology can provide a light source like never before — pure in color, paper thin, and of large area, transforming how we produce both displays and general lighting,” says Vladimir Bulović, the Fariborz Maseeh (1990) Professor of Emerging Technology, principal investigator in the Research Laboratory of Electronics (RLE), director of MIT.nano, and senior author of this study.

He is joined on the paper by lead author Ruiqi Zhang, an electrical engineering and computer science graduate student; Moungi Bawendi, the Lester Wolfe Professor of Chemistry; and other colleagues at MIT and Samsung SAIT. The research appears today in Science Advances.

A blue bottleneck

This paper draws on foundational work by Bawendi, who shared the Nobel Prize in Chemistry in 2023 for discovering and synthesizing quantum dots, and engineering work by Bulović, who joined MIT in 2000, when he began collaborating with Bawendi to make efficient LED displays using quantum dots. 

Conventional LED displays utilize thousands of tiny lightbulbs that generate the red, green, and blue light needed to create the perception of any color on the visible spectrum. More advanced OLED screens, which Bulović was developing through his graduate work at Princeton University, utilize electrically excited, glowing organic molecules instead of light bulbs.

Bulović, Bawendi, and others at MIT sought to replace the organic molecules with quantum dots, which emit purer red, green, and blue light in a more energy-efficient manner.

“With quantum dots, the color quality of the screen would be more visually appealing and more optically flexible. One can mix and match those quantum dot colors more precisely to generate any color that is needed,” says Bulović.

Their collaboration generated a series of inventions on quantum dot LED technologies, leading to the launch of the startup QD Vision, which successfully commercialized the first-ever displays containing quantum dots. In 2016, QD Vision was acquired by Samsung, which incorporated a less efficient form of quantum dot technology into their “QLED” displays.

Although they are more energy-efficient, electrically excited QD-LEDs have still not been commercialized, particularly since the limited lifetime of the blue QD-LED does not meet the requirements of commercial displays.

“The blue quantum dot LEDs are 50 to 100 times less stable than their red and green counterparts. If you use them in an LED display, your TV might last for just a few months before it stops working. We wanted to understand what is different about the blue quantum dot LEDs,” Zhang says.

A nanoscale investigation

He and his collaborators developed a technique to slice a tiny QD-LED in nanoscale-thin slivers, revealing the device cross-section. They examined these cross-sections under extremely powerful microscopes at MIT.nano. This precise method allowed them to see what happens at the nanoscale to the ultrathin layers of materials stacked inside the QD-LED.

They explored the structural and chemical changes that occurred in each layer of red and blue QD-LEDs by comparing cross-sections of freshly made devices to cross-sections of devices that were operated on overdrive. The researchers found that during operation, the three core functional layers that enable blue QD-LEDs to glow are degraded, with modified morphology and reduced thickness. 

The distinct quantum dots also get merged together, losing their shape. This layer thinning and coarsening is caused, in part, by the release of extra hydrogen and oxygen during operation.

“We don’t yet know exactly where these extra elements are coming from — there are so many possibilities. But we definitely don’t want extra hydrogen and oxygen in the device,” Zhang says.

To prevent this degradation, they utilized a technique sometimes adopted by industry. They encapsulated the QD-LEDs with an acrylate-based resin.

They discovered that this encapsulation technique suppresses the release of the hydrogen and oxygen and inhibits some of the degradation that changes the morphology of the layers of the blue QD-LED. 

“For the first time, we have insights into the details of what happens inside these structures of many mixed and layered materials that form the QD-LED. No one knew this before,” Bulović says.

This encapsulation strategy, which is a cost-effective and scalable technique, led to an eightfold improvement in the lifetime of red QD-LEDs and more than a 5,000-fold lifetime improvement in blue QD-LEDs.

The researchers believe the resin prevents the formation of moisture in the cloud of gases that surrounds the quantum dot. That moisture likely causes the QD-LED to degrade. 

However, their experiments revealed that resin encapsulation does not eliminate all sources of degradation. 

The researchers are now exploring the addition of extra layers to QD-LEDs that could further improve efficiency and lifespan. They also plan to build on the lessons learned in this study to increase the stability of QD-LEDs for other applications. 

“This version of quantum dot LEDs would be better than anything that exists now — simpler to make, more efficient, and higher performing. This could open vistas into many more ways of thinking about this technology, not just for the sake of displays or lighting, but also for sensors, lasers, and so on,” says Bulović.

This work was funded by the Samsung Advanced Institute of Technology. The research was carried out, in part, using MIT.nano facilities.