Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

We’ve had a few questions from EFF supporters lately, asking whether the images we use on our blog posts, or on donation and shop items, have been created with AI image generators. We’d like to answer these questions and clarify our internal policy regarding image creation. 

EFF images are all made by human beings, not by automated image generators, with very rare exceptions. This is an internal decision made by our small design team, for the following reasons: 
  

1. Our designers bring knowledge and expertise to the images we create, informed by years of consultation with EFF’s lawyers, technologists and activists. We find that this informed perspective helps make the issues we cover more clear, and more engaging, for our supporters. 
2. The content on our sites is written by human beings, not by bots, and we feel that the images illustrating these posts should be human-made as well. Our supporters come to EFF for honest, trustworthy information from expert human beings, and we want our images to communicate that authenticity as well. 
3. Aesthetic preference: our designers prefer the look, as well as the process, of images made “by hand.” It also gives us more control over the images, including producing multiple versions for different posts. While it can sometimes take a bit longer, we feel the results are more satisfactory and long-lasting.  
4. While it is rare, it is possible for image generators to create images that are under copyright, or understood by some to be under copyright. This could conflict with our use of a Creative Commons Attribution license for all our images. By generating our own images, we avoid any risk of a dispute about copyright infringement, so that we can continue our work promoting digital rights (including the right to fair use of copyrighted materials) without fear of a lawsuit. 

An example of EFF artwork process: sketch and final art

To be as clear as possible, we are now adding a small credit in the lower righthand area of each banner image that will read “Image created by EFF.” As mentioned earlier, there may be rare exceptions, when an EFF designer uses an automatically generated image as a small element in a larger illustration. In these cases, we will indicate that use with additional text, specifying the elements involved, and naming the image generator used.  

We hope that by describing our internal design thinking, we are answering the questions we are getting without confusing anyone about EFF's various and nuanced positions on the issues raised by image generators. As with past technological developments, we continue to defend the rights of technologists to develop these powerful tools, as well as the right of the public to make legal and legitimate use of them. Ultimately, EFF's design team has made a choice we feel is consistent with EFF's brand and look, and it's a decision we think every user gets to make for themselves. 

And don't forget: because all of our images are CC-By, you are free to use, share or remix our any image we create (we ask that you include a credit to EFF). If you need hi-res versions, you can find some on our Flickr page, or you can email us directly with any requests. And you can enjoy some of the art we create on gifts you receive when you donate to EFF!

Donate to EFF

Get awesome human-generated art as a thank you gift!

When I give talks about AI genies, I use this sort of example as a hypothetical. It’s happened.

The story is from Australia. Someone named Andrew tasked OpenClaw to book gym classes for him. And….

Minutes later, his AI agent reported it had discovered a way to book Andrew into classes several weeks in advance, far beyond what was supposed to be possible.

Andrew, who was sitting fourth on a waitlist for a class later that week, asked if it was possible to move him to the top of the list.

The agent came back and told Andrew that it had kicked another gym-goer off the list as part of the testing of its capabilities...

Interesting empirical research: “Black Box Warfare: Human Judgment and Military Decision-Making in the Age of AI.”

Abstract: How is AI transforming decision-making in modern conflict? This study provides a unique empirical window into that question by deploying a high-fidelity replica of an AI decision-support system (DSS) used in military targeting. After reconstructing the interface and functionality of the real-world system, we tested its impact on combat decisions in two experiments involving 2,015 Israeli military personnel. Contrary to widespread fears of automation bias, we find strong evidence of algorithmic aversion, especially in scenarios involving high collateral damage. Yet we also show that integrating “explainable AI” features reduces algorithmic aversion and promotes more thoughtful evaluations of algorithmic recommendations. These findings challenge prevailing assumptions, revealing that trust in military AI is dynamic, varying with individual predispositions, perceived operational stakes, and the informational features of the interface. By grounding normative concerns in empirical evidence, our study offers critical insight into the integration of AI in warfare and underscores the enduring importance of human agency in high-stakes military decision-making...

The authors were informed Monday of the action, which jeopardizes public dissemination of the report on the fast-changing region.

The planned Pecos County facility may generate the most greenhouse gas emissions of any single site in the country.

The legislation will ensure natural disaster relief payments aren't taxed.

The proposal would add pipelines and renewable power generation to the mix of traditional farming, ranching, forestry and other “agricultural land” now subject to disclosure.

But the court said agencies can't be forced to pay out grants, part of a broader fight over whether grant terminations are contractual breaches.

The biggest drag is expected to come from people struggling to work in extreme heat.

Provisional data show that England and Wales have had their driest July in records dating back to 1836.

Scores of firefighters, backed by 43 helicopters, have been deployed to battle wildfires that have burned more than 264,000 acres of forests and other land across the Southeast Asian nation.

The state-run company is expanding into mining of other minerals and generating green power as India inches closer to achieving net-zero emissions goal by 2070.

Physicists across the globe are on a quest for a particle that makes up nearly 85 percent of all matter in the universe, yet no one has ever directly detected it. Jessica Fry, a fifth-year physics PhD candidate in the Laboratory for Nuclear Science (LNS), is one such hunter.

Fry grew up in the San Francisco Bay Area, not far from the SLAC National Accelerator Laboratory. She began dancing at age 3 and by elementary school was competing nationally. On her office desk sits a photograph of her on stage from one of those early recitals, dressed in a ham costume, arms flung wide open — a reminder, she says, that she doesn’t “half-ass” anything.

That includes her fascination with the sciences: when her high school physics teacher handed her a pair of defunct detectors from SLAC and told her to do something with them, she scrounged up a paper from the 1960s using similar equipment, replicated the experiment, and wrote up the project.

“I was hooked,” she says. “I could answer philosophical questions about how time and space interact with something I could physically touch. That just blew my mind.”

Parallel pursuits

Fry went on to study at Stanford University, where she double majored in physics and theater and performance studies, committed to both.

During her sophomore year, a talent agent she had met through the dance competition circuit called: A Broadway production of David Henry Hwang’s “M. Butterfly” was casting. But for Fry, it was midterm season. She flew cross-country to New York for the audition and immediately returned home in time for exams. Then, a month of deafening silence. Fry, assuming she’d been rejected, secured a summer research position in Switzerland at CERN, the European Laboratory for Particle Physics. Then she got the call: She had been selected for the Broadway show. After finishing her summer research in Geneva, she flew directly to New York to begin rehearsals.

Fry took two years away from Stanford to perform, training vigorously in ballet, contemporary, and jazz dance. She also learned traditional Māori dance, Peking opera-style movement, and stage combat. She says, “All of those skills go toward the central theme of: How do I tell a story in the best possible way?”

But slowly, she noticed something in herself and the people around her. Every couple of months there was another round of auditions, another round of external judgment from strangers with the power to control her future. One “occupational hazard of theater,” Fry explains, “is beginning to trust someone else’s opinion of you more than your own.” She struggled to reconcile her love for dance as an art with what dance as a career was doing to her confidence and sense of self.

“It was turning me into someone I didn’t want to be,” she says. “It took a lot of reflection to recognize that.”

Having hit a crossroads with her dance career, she made the aching decision to return to Stanford to finish her degrees. Shortly after, she applied to graduate programs — MIT among them.

“MIT’s Laboratory for Nuclear Science alone is the size of most other institutions’ entire physics departments,” she notes. “Similar to how, at CERN, there’s just this buzz, this scientific energy. I felt that when I visited MIT.”

Searching for a signal in the dark

Now Fry channels that scientific energy into tackling an enduring phenomenon that has long confounded astrophysicists: dark matter. For nearly a century, scientists have observed that the universe contains far more matter than we can see — that the way galaxies move and form cannot be explained by visible matter alone. Dark matter emits no light and interacts with next to nothing, yet exerts a gravitational pull on almost everything. Fry is searching for what she believes is the field’s best theoretical candidate: the axion.

The axion, should it exist, is ultralight and many orders of magnitude smaller than an electron. At that scale, it behaves less like a discrete particle and more like a coherent wave that permeates the galaxy and clusters gravitationally around matter.

Fry is working on two experiments to detect axions. The first, already operating at MIT, is called ABRACADABRA: A Broadband/Resonant Approach to Cosmic Axion Detection with a Bayesian B-Ring Apparatus. “I had to practice saying that many times my first year,” she grins. The second, which she is currently helping to build at Stanford, her alma mater, is called DMRadio, short for Dark Matter Radio. Both operate on the same principle: In the presence of a strong magnetic field, axions should produce a faint, oscillating electric current.

“Think about two waves in the ocean — when they collide, they create a rip current. We are looking for that rip current,” she says.

The current is amplified through resonance using circuit components and quantum amplifiers. Different axion masses correspond to different frequencies, so the detector is tuned systematically across the full range, much like tuning a car radio. The problem is that even the amplified signal is buried in noise; thermal fluctuations, environmental interference, and other electrical activity all cloud it. Thanks to theorists in the field, Fry knows the shape of what she is looking for, but the axion’s mass and interaction strength remain unknown.

“It’s a hard problem. But it’s a tractable one because the shape of the axion signal is so distinctive. There’s basically nothing else that looks like it,” she says. “It is a fun hunt.”

Tuning in

Fry works with professor of physics Lindley Winslow, who leads the Neutrino and Dark Matter Group within MIT’s LNS. Winslow sees in her advisee a quality she recognizes from her own life.

“We share in our history a turning point, a choice between two great passions and a difference in the direction our lives could have taken,” she says. “Those lives-not-lived continue to shape how we approach our physics . … I see this in her work: a drive to always do it better, a demand for feedback, and then when the curtain rises, the fearlessness to deliver.”

The Neutrino and Dark Matter Group consists of four principal investigators whose collaborative structure Fry describes as one of the best features of the department, one that has pushed her well beyond her own subfield. Outside the lab, in a studio in an old church near Harvard, she still dances. “At the end of a long day of using my brain,” she says, “I love just being in my body.”

Still in the final stretch of her program, Fry has already earned a spot on the Forbes 30 Under 30 Science 2026 list. For now, she is focused on completing the data analysis for DMRadio and seeing the detector to the finish line. What comes next — postdoctoral positions, her own lab group, possibly her own detector — she considers with a clarity she attributes, in part, to having already made a harder decision once before.

“I realized that doing physics is going to make me happy and allow me to make the impact I want to,” she says. “I keep checking in on that. I don’t want to just chase prestige and go to the end of the road because I can. I want to make sure it’s what I want to do.”

She is convinced dark matter will be discovered within her lifetime. She says “when,” not “if.” The detection approach she has spent five years refining is, she thinks, among the most promising ones in the field. “We just need to keep tuning.”

Imagine if McDonald’s could use trademark law to control how you use the term “fast food.” Or if the Canadian government could stop you from using the word “Canada” in the title of a book about the country and its people. That wouldn’t just be absurd; it would be an unacceptable obstacle to criticism of and commentary about those institutions. Yet the Church of Jesus Christ of Latter-day Saints (the “LDS Church”) has a track record of claiming exactly that kind of authority over the word “Mormon,” using the threat of expensive litigation to pressure speakers into compliance.

We at EFF have opposed the LDS Church’s abuse of trademark law for over a decade. In 2014, we filed an amicus brief when the church sued an online dating service for church members called Mormon Match. In 2016, it threatened legal action against our client the Mormon Mental Health Association, a nonprofit association for mental health professionals who work with members of Mormon faiths. In 2025, the church tried to pressure our client Burke Sorenson into changing the name of his Mormon News Roundup podcast. Now, the LDS Church has brought a lawsuit over a podcast called Mormon Stories that examines Mormonism and Mormon culture. With the help of attorneys at Ballard Spahr, EFF has filed an amicus brief in the case.

Our brief urges the district court to dismiss the case as soon as possible. Trademark is supposed to be about helping consumers identify the sources of the products they buy, not controlling criticism. That’s why our brief asks the court to use a test that’s more protective of speech than what’s applied in most trademark cases. This test, known as the Rogers test, has been adopted by many courts (but not yet this one) for cases where someone is using a trademark as part of an expressive work, rather than just as a brand name. We explain to the court that the Rogers test is an important First Amendment safeguard in part because it makes it easier to throw out meritless trademark claims before the most expensive parts of litigation, allowing more speakers to confidently stand up for their rights.

Our brief goes on to explain that First Amendment safeguards are especially important in cases like this one, where a plaintiff is seeking to control the use of a common term for its common meaning. Trademark law isn’t even supposed to extend to generic terms, and for good reason. Otherwise, we risk giving trademark owners power to control discussion and debate over entire topics.

It’s about time that a court shut down the LDS Church’s trademark bullying. We hope the court will do so here, while also taking the opportunity to endorse the Rogers test.

Artificial intelligence models are jacks of many trades, including writing, generating images, and creating 3D models. But they aren’t as helpful when it comes to testing robots or designs for vehicles in diverse environments, since they don’t understand physics as well as they do pixels or text.

To build an AI system that can reliably simulate a variety of physical scenarios, engineers need a range of physics data at a scale that isn’t yet feasible. That’s because it’s very time-consuming to get neural networks just a few data points they can understand. They rely on algorithms called “numerical solvers” to calculate physical properties at different points of a 3D shape. It’s a thorough process, but it takes so long that it limits how much data you’ll have to, say, test if your plane designs are safe and aerodynamic.

A new pre-training approach known as “GeoPT,” deveoped by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Tsinghua University, gives simulation models a chance to learn physics in a broader, more efficient way. It virtually reenacts everyday mechanical interactions in 3D, showing how particles stop when reaching some part of an object. These simulations give the models a sense of how physics works, helping them model the real world more accurately, reach peak performance twice as fast, and train on up to 60 percent less data compared to leading models.

Soon, the project could help engineers predict how vehicles (like cars and planes), everyday items (including chairs and containers), and robots respond to various physical elements, such as wind, water, and collisions. The researchers believe their work could also be a step toward a physics foundation model, a backbone system trained on lots of data that can help AI tools generalize to different tasks.

“We believe physics is the third modality for AI models, after text and pixels,” says MIT PhD student and CSAIL researcher Minghao Guo, a co-lead author on a paper introducing GeoPT. “Our general-purpose model has the versatility to help build a world model for physics. Many models, such as those that generate robotics data and videos, are already well-versed in textual and visual data, but with physical accuracy, they’ll get more-realistic results.”

Easy to use

To use GeoPT, users simply upload 3D models of objects like battleships, passenger airplanes, and trucks, and specify the direction and speed of the force they want to simulate. The result is a kind of heat map showing how the object will be affected in different places. If you know the speed and direction (velocity) of the force you’re looking to simulate, you can capture it in GeoPT. This comes in handy when you want to simulate things like how a car would look after crashing into a wall, the ways light bounces around objects, and whether a boat stays afloat over turbulent waves.

But how does GeoPT “get” physics so well? Its knowledge comes from “synthetic dynamics,” a series of interactions between small particles and complex 3D shapes. GeoPT studied 1.3 million samples of synthetic dynamics, in which tiny spheres moved at various speeds and angles until stopping at a certain point on the object.

These particles basically “stick” to an object once they make contact, instead of moving through or bouncing off. Picture learning about physical interactions using marbles and action figures — similarly, simulation models can use synthetic dynamics to gain a feel for physics before they train on labeled data.

Industry success

The researchers found that GeoPT was particularly skilled at simulating industrial scenarios, as it outperformed state-of-the-art simulation models across benchmarks. The common thread: It reached peak performance faster than other tools, while needing significantly fewer labeled data.

On a dataset of complex 3D shapes and their responses to wind currents and surface pressure, for example, GeoPT surpassed state-of-the-art models in speed, accuracy, and efficiency. It had similar triumphs in speed and accuracy in capturing how fighter jets responded to wind. When GeoPT tested how the hull of a boat handled both air and waves, it required 60 percent fewer labeled data to capture both physical forces and reached peak accuracy four times faster than top baselines.

The system even succeeded at simulating how different types of cars look after colliding with another object. It correctly predicted how 3D vehicles would deform while using less data than state-of-the-art baselines. Likewise, its simulations of how light would pass through what was essentially a toy rabbit were accurate, despite never training on that 3D model or light physics beforehand.

“If your model performs well on industrial benchmarks, that means it can solve the hardest physics tasks,” says co-lead author Haixu Wu, an MIT postdoc and CSAIL researcher. “GeoPT was making high-fidelity simulations with over 100 million mesh points in seconds. This could make the tool extremely helpful for engineers hoping to test out blueprints for vehicles without needing to run so many physical experiments.”

The researchers add that their system is only a preview of the kind of physics world model they’ve been working toward. The team hopes to scale up their system, training on even more shapes and simulating more complex physical phenomena. For example, a more in-depth approach could help model weather patterns, test out different materials, and generate realistic videos.

“Using synthetic dynamics data is an exciting paradigm for imbuing physics into foundation models,” says Fei Sha, AI research scientist at Meta, who wasn’t involved in the research. “It challenges the traditional wisdom that physics and geometry are necessarily entangled in computation, and one must acquire costly and specialized data. The demonstrated success in a wide range of application domains leads us to this important juncture: We are ready to build physics foundation models, now and fast."

Wu and Guo wrote the paper with MIT CSAIL colleagues including Zongyi Li, a postdoc in the lab; Zhiyang (Frank) Dou, a CSAIL affiliate and MIT PhD student in electrical engineering and computer science (EECS); Kaiming He, a principal investigator in the lab, associate professor of EECS, and a distinguished scientist at Google DeepMind; and senior author Wojciech Matusik, the Joan and Irwin M. (1957) Jacobs Professor of EECS and a CSAIL principal investigator. Tsinghua University Associate Professor Mingsheng Long was also a co-author. The team presented the paper at the International Conference on Machine Learning in July.

The researchers’ work was supported, in part, by Neural Modular Physics Twin for Robotics.

On Earth, the processes behind navigation are made nearly invisible by global positioning satellites (GPS). In cislunar space — the region between Earth and the moon — spacecraft do not have that kind of always-available positioning service. Missions beyond geosynchronous Earth orbit still rely heavily on NASA's Deep Space Network (DSN), an accurate but limited Earth-based international array of radio antennas shared across many missions and nations.

Because all DSN sites are located on Earth, their separation is small compared with the scale of cislunar space, which limits the angular baselines available for orbit determination. Therefore, precisely estimating orbits for distant spacecraft can take hours, and DSN supports only a few missions at a time. In addition, DSN requires user spacecraft to actively emit signals for measurement, unlike GPS, which passively sends data for users to receive.

The Laser Communications Group and Advanced Capabilities and Technologies Group at MIT Lincoln Laboratory are developing a concept called the Light High-Orbit Utility Signal Emitter (LightHOUSE) to help overcome these limitations. LightHOUSE would use a small constellation of satellites in high-altitude orbits as cooperative optical beacons. These beacons would exchange timing and communication signals with user spacecraft and use imaging against the stellar background to estimate each spacecraft's three-dimensional position and velocity. By providing timely, independent navigation data across cislunar space, LightHOUSE could reduce the need for corrective maneuvers, preserve spacecraft propellant, lessen the burden on onboard navigation sensors, and ease demand on existing ground-based systems.

“Satellites in cislunar space have limited access to support resources, even though orbits at and beyond the geosynchronous belt are increasingly important for various missions,” says Aaron Greenberg, a technical staff member in the Laser Communications Group. “The moon is reemerging as a strategic priority for national security. Nearly all space missions require some degree of precision navigation and timing, but no global positioning system exists in this domain. Here is where LightHOUSE is intended to step in, expanding critical and reliable communication and navigation services across this vast region.”

LightHOUSE would use free-space optical communications — laser links through space — rather than relying solely on radio-frequency systems. The concept builds on laboratory work demonstrated through NASA-sponsored programs such as TBIRD and O2O, as well as the Optical Time Transfer for Resilient Satellite Communications Networks project led by the Laser Communications Group with funding from the laboratory's internally administered R&D portfolio in optical systems technology.

“This concept hinges on a cooperative ranging capability enabled by free-space optical communications,” says Timothy Yarnall, an associate leader of the Laser Communications Group. “This technology area is one in which the laboratory is a global leader, as evidenced by the recent O2O success during Artemis II. The laboratory's experience with radiation hardening of digital focal plane array technology will also enable the sensitive receivers and star cameras — like the camera built by the Advanced Imager Technology Group for NASA's Psyche mission — that this concept relies upon.”

LightHOUSE beacons would be based in ultrahigh orbits, up to roughly 1 million miles in altitude. These high orbits replicate the angular diversity of GPS signals for users across cislunar volumes. They would also allow communication with spacecraft on the far side of the moon as viewed from Earth, preventing blackouts like the 40-minute period when Artemis II passed behind the moon.

Borrowing from the GPS philosophy, LightHOUSE is designed to place most of the technical burden on the beacon satellites, rather than on user spacecraft. The beacons would carry telescopes with tens-of-centimeter diameters and laser transmitters in the tens-of-watts range, while users would need only centimeter-scale apertures and tens-of-milliwatt lasers. The central engineering challenge is making that asymmetry work across cislunar space.

“From a design perspective, a major challenge will be making these services as easily accessible as possible to all potential users. The designed systems would be highly asymmetric, with LightHOUSE beacons taking on most technological and operational demands necessary to close links over the entire cislunar domain,” says Seth Trotz, a senior staff member in the Advanced Capabilities and Technologies Group.

Obtaining precise position measurements over such distances — combining optical communications with high-resolution imaging when beacons and user spacecraft are more than half a million miles from Earth — is itself a significant technical hurdle.

The team is now refining the system concept through analysis, simulation, and laboratory experimentation. In the near term, they plan to publish a detailed architecture for providing navigation data to LightHOUSE users. Longer term, the goal is to make navigation beyond geosynchronous altitudes routine, reliable, and accessible for a broad range of users, supporting Artemis and the growing wave of missions to follow in cislunar space.

This work is sponsored by the undersecretary of war for research and engineering through the laboratory's internally administered R&D portfolio in sensing and communications. A full-scale system would require substantial investment, potentially on the order of hundreds of millions of dollars; for comparison, the operating budget of GPS is $1.8 billion per year, and a single DSN dish costs roughly $85-100 million.

Access to accurate information about reproductive and maternal health can be critical. But on Meta's platforms, simply talking about prescription medication, abortion care, or one's own medical experiences can be enough to trigger content removals and account restrictions.

That's why EFF recently submitted a public comment to the Meta Oversight Board in its consideration of a case involving an Instagram post about prescription drugs during pregnancy and childbirth. The case touches upon a topic we’ve been documenting for some time; last year we collected stories from individuals who had experienced censorship of reproductive health information on various platforms. Meta in particular stood out: Its moderation systems routinely fail to distinguish between prohibited drug transactions and legitimate discussion of medications, including educational information and people's firsthand experiences with healthcare.

Through our Stop Censoring Abortion project, EFF collected nearly 100 submissions from healthcare providers, clinics, educators, advocates, researchers, and others whose reproductive health content had been removed or suppressed by social media platforms. What we found was alarming: systemic over-enforcement, confusing policies, arbitrary takedowns, sudden account bans, de-ranking, and appeals that too often went nowhere.

Talking About Medication Isn't the Same as Selling It

In almost every case we reviewed, the censored posts and accounts did not actually violate the platforms' stated rules. Meta frequently cited its Restricted Goods and Services policy, which prohibits attempts to buy, sell, trade, donate, gift, or request pharmaceutical drugs. But the content EFF documented overwhelmingly consisted of factual or educational information—not attempts to sell or distribute drugs.

The consequences were significant. For example, the Miscarriage+Abortion Hotline had its Instagram account restricted and posts removed even though it was providing information about legally obtaining medication rather than offering pharmaceuticals for sale. Red River Women's Clinic and the RISE reproductive health research center at Emory University had accounts locked after posting about mifepristone.

Other users reported having their content quietly de-ranked or “shadowbanned,” limiting its reach without giving them meaningful notice or recourse. We believe educational content and people's experiences involving reproductive healthcare and medication should not be suppressed in this way.

And when Meta gets these decisions wrong, the appeals process too often fails to fix them. In several cases EFF documented, accounts were restored only after journalists drew attention to the problem or someone with a personal connection inside Meta intervened. A moderation system shouldn't require knowing the right person to get an erroneous decision reversed.

Meta Can—and Must—Do Better

Our submission calls on Meta to make five changes—the same five changes we asked for last year.

First, Meta should publish clear, understandable policies so users can know what content is permitted and what might result in removal, downranking, or account suspension. Second, those rules must be enforced consistently and fairly. Third, Meta must provide meaningful explanations for enforcement decisions, including what rule was violated and how users can appeal. Fourth, users need a functional appeals system that doesn't depend on insider access.

Finally, Meta should expand human review. Reproductive healthcare is precisely the sort of nuanced and context-dependent subject that automated moderation systems struggle to understand. As our research shows, automated systems can mistake education for drug sales, misinterpret terminology, overlook cultural and political context, and even classify legitimate advocacy as dangerous content. Human moderators should therefore play a greater role when automated systems flag sensitive healthcare information or political expression.

Meta has chosen to allow discussion of reproductive healthcare, including abortion, on its platforms. That commitment means little if its moderation systems nevertheless prevent people from accessing or sharing that information.

At a moment when reproductive rights are under attack around the world, the stakes are particularly high. Restricting access to essential healthcare information can have profound consequences, especially for people who already face barriers to reproductive care.

Users deserve a system in which rules aren't applied arbitrarily, appeals actually work, and vital health information isn't silenced because an automated system failed to understand its context. Meta can—and must—do better.

You can read our comment in full below.

This is good:

Post-quantum cryptography is now one pip-install away for the entire Python ecosystem. With funding from the Sovereign Tech Agency, we implemented support for ML-KEM, the NIST-standard key-establishment primitive, and ML-DSA, the NIST-standard digital-signature primitive, in pyca/cryptography.

Remember, the reason to do this now is because there’s no emergency. And because you will make your systems crypto agile, which is always a good idea.

Some environmentalists are looking to rebuild a cordial relationship with Gov. Kathy Hochul as they look for alignment after a bitter fight over the state’s climate law.