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.

Eminent theoretical physicist Jeffrey Goldstone, an MIT professor emeritus, Dirac Medalist, and holder of the Department of Physics’ Cecil and Ida Green chair, died Oct. 12 at age 93. 

A member of the MIT physics community for 49 years, Goldstone was especially known for his influential work in quantum field theory as well as contributions to quantum optimization algorithms. Notably, he is the namesake of the Goldstone boson and the related Goldstone theorem.

“Jeffrey was a giant of theoretical physics who will be long remembered and very much missed,” says Tracy Slatyer, director of the MIT Center for Theoretical Physics – a Leinweber Institute (CTP-LI) and professor of physics. “He contributed greatly to the CTP-LI over the years, and it was an honor to have him as a colleague.”

“Goldstone was a brilliant theoretical physicist and a charming, yet diffident and remarkably humble human being,” says Frank Wilczek, a CTP-LI colleague who is the Herman Feshbach Professor of Physics, Emeritus, and a 2004 Nobel laureate. “He made renowned, fundamental contributions to diverse areas of physics, ranging from condensed matter and quantum computing to string theory. Throughout, you can perceive a consistent style: enormous mathematical talent brought to bear on important, concrete, sharply posed physical problems.”

Goldstone is renowned for his remarkable range of groundbreaking discoveries. He began his career as a nuclear theorist. In his early work on nuclear matter with Hans Bethe, he developed the use of Feynman diagrams for non-relativistic many-fermion systems.  

He later turned to particle physics, investigating relativistic field theories with spontaneously broken symmetries. Building on ideas inspired by superconductivity, he demonstrated that spontaneously broken continuous symmetries must be accompanied by massless, spinless particles — now widely known as Goldstone bosons (or Nambu-Goldstone bosons, in recognition of Yoichiro Nambu’s earlier related insights). 

In subsequent work with Abdus Salam and Steven Weinberg, Goldstone formalized and generalized this result, which became known as the Goldstone theorem. With Claudio Rebbi, Charles Thorn, and Peter Goddard, he worked out the light-cone quantization of relativistic strings, a foundational result in string theory. Goldstone also worked with Roman Jackiw and Frank Wilczek on solitons in quantum field theory.  

Goldstone was born in 1933 in Manchester, England. He earned his PhD from Trinity College at the University of Cambridge in 1958 and completed postdoctoral work at Cambridge, Harvard University, CERN, and Copenhagen University. After shifting his focus to particle theory, he joined the faculty at Cambridge in 1962. He moved to MIT as a professor of physics in 1977 and as a researcher in the Laboratory for Nuclear Science, and was appointed the Cecil and Ida Green Professor of Physics in 1983. Goldstone served as director of the Center for Theoretical Physics (now known as the CTP-LI) from 1983 to 1989.  

“Goldstone’s name will always be associated with spontaneous symmetry breaking, where 'Goldstone’s theorem' and 'Goldstone bosons' feature prominently,” Wilczek notes. “His early, seminal papers relating the mathematical concept to physical phenomena gave firm foundations upon which people built fruitful theories of magnetism, meson physics, and eventually the Standard Model and early universe cosmology.

“Jeffrey Goldstone’s ideas are so deeply woven into physics that it takes an occasion like his passing to remind us how much we owe him. I had the pleasure to work with Jeffrey in the early days of fractional charge. The short paper we wrote together is one that I’m especially proud of. It contains the seeds of many later developments. Thank you, Jeffrey, for that fond memory.”

Alan Guth ’69, PhD ’72, MIT’s Victor F. Weisskopf Professor of Physics, Post-Tenure, says, “Jeffrey was a wonderful friend and colleague, and a towering figure in theoretical physics. When I was trying to arrange to come back to MIT after being a postdoc for nine years at other places, it was Jeffrey who I contacted to try to make this happen. When I first learned in 1981 that Andrei Linde had figured out how to make cosmic inflation work, in the form known as 'new inflation,’ I remember that I ran into Jeffrey's office to share my excitement. I have learned a great deal from Jeffrey over the years, and will forever have warm memories of our friendship.”

“Jeffrey Goldstone was a theoretical physicist of rare talent and impact,” says David Kaiser, MIT professor of physics and the Germeshausen Professor of the History of Science. “His landmark article, 'Field theories with superconductor solutions,' published in 1961, helped to launch the modern era of symmetry breaking in high-energy physics. Building on insights from neighboring fields such as condensed-matter physics, Goldstone demonstrated that for any model in which a continuous symmetry is spontaneously broken, there will emerge zero-mass, zero-spin particles now known as 'Goldstone bosons.' His watershed study, and several influential follow-up articles, have become central to our Standard Model of particle physics, with further application as far afield as early-universe cosmology to spin waves in magnetic materials and quantized vibrations in excited fluids. Later in his career, Goldstone turned his attention to quantum information science, helping to construct several novel algorithms for quantum computation. 

“Typically reserved, his close friends admired his droll humor. Writing to Harvard physicist Sidney Coleman in November 1976, for example, Goldstone declined Coleman’s invitation to attend an upcoming physics workshop. 'I expect to be in total confusion during January [1977] and wish to simplify my life,’ Goldstone replied. 'Forty-three years in the womb’ — including decades at his alma mater, the University of Cambridge — 'have unfitted me for coping with normal trivial details of survival. It’s probably a good thing I’m coming out, but the birth pangs are hell.’ 

“The birthing event to which Goldstone referred was his move to MIT to join the Physics Department faculty in 1977. Thank goodness he braved the move. Over nearly 50 years, Jeffrey Goldstone enriched our Department of Physics at MIT, and especially our Center for Theoretical Physics – A Leinweber Institute, with his deep insights and gentle manner.”

Even after retiring from MIT, he remained active in research for the next two decades, working in quantum computation. 

Edward Farhi, professor emeritus of physics and a frequent collaborator for over 20 years, describes Goldstone’s contribution to the field from its early stages as “very significant.”

“FFG [Farhi, Goldstone, and Samuel Gutmann] teamed up with Donner Professor of Mathematics Michael Sipser and showed that there was a problem which could not be sped up with a quantum computer, learning parity in the oracle setting,” Farhi says. “This was the first demonstration of this kind of limitation, although the same week another group proved the same thing by a different method. In 2000 FGG, along with Sipser, introduced the concept of Quantum Computation by Adiabatic Evolution. This is an all-purpose approach to optimization and was later shown to be universal, which means that any quantum computation can be encoded as quantum adiabatic. Furthermore, the company D-wave was founded to run this algorithm. D-wave is the progenitor of all of the superconducting hardware efforts such at the ones at Google and IBM. We later showed that using scattering theory a quantum computer could determine who wins a game provably faster than the best classical algorithm for this task.

“FGG introduced the Quantum Approximate Optimization Algorithm. This was the first time a general purpose quantum optimizer could provide worst-case performance guarantees for combinatorial optimization problems. These guarantees as of now (with one exception) do not beat the best classical. Still, this paper has over 6,000 citations, making QAOA one the most-studied quantum algorithms.

“In 2019, FGG and Leo Zhou applied the QAOA to the Sherrington-Kirkpatrick model. We showed how to analyze performance in the infinite size limit at any depth. Our work and others indicated that as depth increases with optimal parameters, the QAOA would approach the celebrated Parisi value. Recently the Open AI math dump, the conjecture that the QAOA would hit Parisi, was proven by the AI. I bet Jeffrey would have been surprised and impressed!”

Jeffrey Goldstone was a fellow of the American Physical Society Fellow, the Royal Society, and the American Academy of Arts and Sciences, and he was an honorary fellow of Trinity College, Cambridge. 

His honors included the Guthrie Medal of the Institute of Physics, the Dannie Heineman Prize for Mathematical Physics of the APS, and with Stanley Mandelstam the Dirac Medal (1991) of the Abdus Salam International Centre for Theoretical Physics, awarded for “his fundamental clarification of the phenomenon of spontaneous symmetry violation in relativistic field theory.”

He is survived by his son Andrew Goldstone, associate professor of English at Rutgers University’s New Brunswick campus; his daughter-in-law Anne Dewitt, a clinical associate professor at New York University’s Gallatin School of Individualized Study; and his granddaughter, Helen. His family is planning a memorial in 2027. 

It’s a bad one, and it’s been in the code for a decade:

A newly disclosed flaw involving OpenSSH and musl, dubbed “*sshole,” allows attackers to log in to certain Alpine Linux and other musl-based Linux accounts configured to block password authentication simply by entering an oversized password.

“Oversized” means longer than 256 bytes. Details.

Slashdot thread. And another news article.

MIT researchers will lead an up to $53 million effort, awarded by the U.S. Advanced Research Projects Agency for Health (ARPA-H) as part of its STREAM (Systems for Tracking and Resilience in Efficient Agricultural-input Management) program, to develop next-generation weed killers that help farmers and protect human health. 

More than 1 billion pounds of herbicide are used in the United States every year. Herbicides help farmers by preventing significant crop loss due to weeds, and they enable regenerative farming practices that protect the soil, but some weeds are becoming resistant to the most common herbicides. 

Exposure to herbicides may contribute to increased risk of cancer, metabolic and endocrine disorders, and other adverse health outcomes. Farm workers can be exposed from frequent herbicide use, but many U.S. residents are also exposed to these chemicals in small amounts through food and drinking water. 

The MIT-led research team will develop new generative artificial intelligence tools to help them discover herbicide alternatives that are more effective and developed with human health in mind. Such alternatives could also help to reduce U.S. dependency on imported phosphorus, which is a critical ingredient in a major herbicide, glyphosate.

“With herbicides like glyphosate and atrazine, there’s a real crisis because of resistance mutations that are coming,” says Christopher Voigt, head of MIT’s Department of Biological Engineering. “And if you combine the resistance mutations with issues around national security of the supply chain of the precursors to herbicides, we could find ourselves without these core agrochemicals that are absolutely essential for getting the high crop yields and economical prices that the United States now sees.”

Voigt is the principal investigator for the project, known as Precision Learning for On-target Weed control (PLOW). The team is also expected to include researchers from the Broad Institute of MIT and Harvard, the University of North Carolina at Chapel Hill, the University of Tennessee at Knoxville, the University of Illinois at Urbana-Champaign, and Robigo.

The researchers are grouped into three teams that will tackle different aspects of the project, aligned with the STREAM Technical Areas. The generative AI group will develop models that can predict molecules — either chemical compounds or small interfering RNA (siRNA) — that disrupt the same plant enzymes targeted by glyphosate and other herbicides.

Glyphosate kills weeds by interfering with an enzyme called EPSP synthase, which plants need to produce several critical amino acids. After many years of herbicide use, some weeds have begun to acquire genetic mutations that allow them to become resistant to those herbicides, which makes them less effective and poses a significant threat to crop yields.

“There’s a real need for new herbicides because of resistance mutations that have been arising,” Voigt says. “There are also concerns about herbicides’ possible impacts on the environment and on humans, so it’s important to identify alternative ways of making herbicides, which as a general class of molecule are really critical to agriculture.”

The MIT-led team plans to design generative AI models that will be able to predict molecules that can help prevent resistance from arising. The models will also optimize for other features such as low cost of synthesis, low risk of inducing resistance, and low risk of harming humans or causing ecological damage. 

The group developing these AI models is led by Connor Coley, the Warren K. Lewis Associate Professor of Chemical Engineering at MIT; Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT; and Alexander Tropsha, the K.H. Lee Distinguished Professor in the UNC Eshelman School of Pharmacy.

The models are expected to generate billions of possible candidates, which will then be tested by a second research team. This high-throughput screening group will be led by Angela Koehler, the Charles W. and Jennifer C. Johnson Professor of Biological Engineering at MIT, and Jaime Cheah, the associate director of scientific outreach and collaborative screening in the Center for the Development of Therapeutics at the Broad Institute.

“Those libraries will be screened both to identify those molecules that hit their target, but also to then provide all of that screening data back to the computational approach in order to improve it,” Voigt says.

Once the candidate pools are narrowed down, researchers on a third team will begin greenhouse studies to evaluate how each molecule performs under different agricultural conditions. That team will be led by Neal Stewart and Scott Lenaghan, co-directors of the Center for Agricultural Synthetic Biology at UTK; Patrick Tranel, the Tom and Peggy Ainsworth Professor of Crop Sciences at the University of Illinois; Tim Willson, the Harold Kohn Distinguished Professor at the UNC Eshelman School of Pharmacy; and Andee Wallace PhD ’20, CEO and founder of Robigo. 

The first greenhouse tests of initial RNAi candidates are expected to be complete by the end of the first year of the program.

As part of this project, the research team will also develop living microbial systems that degrade agrochemicals before they reach food, soil, or waterways. Engineered bacteria, fungi, and srRNA will convert chemicals and formulation ingredients into biomass, providing a scalable strategy to reduce environmental and human exposure.

This research is funded by the Advanced Research Projects Agency for Health (ARPA-H) under agreement number 140D042690030. The views and conclusions contained in this article are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the United States government.

The United States used to be the world leader in metal casting, a process where liquid hot metal is poured into a mold to solidify into a desired shape. But more than 70 percent of American metal casting facilities have closed since the industry’s peak in the 1950s. Today, China and India dominate global metal casting production, which is critical for making the components of jet engines, nuclear and gas turbines, medical devices, and more.

Fabri is trying to revitalize American metal casting. The company, founded by Steven Davis ’24; Thomas Cole ’91, MBA ’13; and Pieter Coulier, is building a new kind of metal casting foundry that uses additive manufacturing, software, and vertical integration to eliminate many of the most labor-intensive steps associated with traditional metal casting. 

Fabri is already producing parts for a growing list of customers at its 16,000-square-foot plant in Billerica, Massachusetts, where it is applying its approach to one of the most multidisciplinary and challenging types of metal casting, known as investment casting. The company is working with Raytheon, Lockheed Martin, and Sikorsky Aircraft, among others, and is moving through qualification to make parts for the most critical aerospace and defense applications.

Fabri’s first foundry was designed to be quickly replicated. Even as it significantly expands capacity at its current plant, demand from customers is driving the company to build a far larger facility in 2027 that will produce bigger parts at higher volume as customer programs move into production.

“Metal casting is one of the oldest and most prevalent manufacturing methods in the world,” Davis says. “It’s a uniquely difficult process because there are so many different steps. There’s so much domain knowledge and experience you need to understand it. If we get cut off from the world’s casting capacity, that means no more pots and pans, no more storm drains and other basic things, but also no more aircraft. Lots of medical devices, like titanium hip implants, are cast. Power generation also depends on metal parts. These are very critical industries that we need to continue to produce.”

Faster metal production

Davis grew up competing in robotics competitions like BattleBots. The biggest challenge for his small, underfunded teams was getting the parts to make the robots.

Davis arrived at MIT in 2020, where he majored in mechanical engineering. As part of a summer internship after his sophomore year, he worked at Relativity Space, which builds reusable rockets. There he saw multi-million-dollar projects being delayed by the same hurdles he faced in robotics competitions. His team would wait weeks for expensive machines to make parts.

“I realized the problem was scale,” Davis says. “These production processes couldn’t even scale to aerospace, so they weren’t going to scale to far larger industries.”

When Davis returned to MIT that fall, he worked on adapting the precision and flexibility of 3D printing to producing metal parts. Davis also took several classes taught by John Hart, MIT’s Class of 1922 Professor and head of the Department of Mechanical Engineering. Hart co-founded several additive manufacturing companies including VulcanForms and Desktop Metal. He continues to advise Fabri.

“I was very interested in finding the best uses of additive manufacturing,” Davis recalls. “A lot of the most successful companies weren’t making end parts; they were making tools. My favorite example is Invisalign. They’re not printing the dental liners: They are printing the molds that make the aligners.”

Davis began thinking about starting a company roughly midway through his bachelor’s degree at MIT. He met Cole through his MIT network. Cole had founded a fully automated CNC machining company making dental implants that was acquired by AstraZeneca. Pieter Coulier, Fabri’s third co-founder, previously developed the simulation and metal 3D-printing algorithms at Velo3D that enabled SpaceX’s Raptor engines to be printable.

Davis also began visiting foundries, where he repeatedly heard their biggest bottleneck was labor.

“They all have huge ‘Help Wanted’ signs out front,” Davis says. “Capacity is going down. That’s why we realized automation had to be a core part of our thesis. According to the American Foundry Society, these workers are retiring at a rate of 3-to-1. It’s really hard to find these foundry engineers who understand what goes into the process and can design these molds. We have customers tell us they had an engineer retire and now they can’t make certain parts anymore. We want everything we learn to be encoded into our software forever.”

Investment casting is a complex, multistep process.

“Traditional investment casting starts by cutting a hard tool out of steel,” Davis explains. “Then you injection mold wax patterns into it. Those wax parts get glued together into what’s known as a tree. Then you dip the tree into liquid loaded with ceramic powder, you stucco it with sand, dry it, and repeat the process seven or eight times. That’s how you get the ceramic mold to pour your steel into to make the part.”

Fabri’s process starts with the part’s computer-aided design (CAD) file. The company’s proprietary software, FoundryOS, designs a pattern for each part, which Fabri prints in resin using established techniques including digital light processing (DLP) and stereolithography. The printed pattern is dipped in ceramic slurry to build the shell that molten metal is poured into. FoundryOS incorporates data from every step and uses machine learning models to improve the process and the parts with each casting.

“By printing that mold right off the bat, we remove the need for all those early steps,” Davis explains. “We fully automate the creation of the [complex patterns and channels] needed for making that tree. Instead, we print it all in one piece and consolidate a lot of that work.”

Davis says traditional tooling can cost upward of $1 million for complex enough designs and take many months to make — a big reason companies have to wait so long for parts.

Davis built the first prototype of Fabri’s printer in the basement of his MIT fraternity using funds from the MIT Sandbox Innovation Fund. After subleasing a space in Ashland, Massachusetts, Fabri was pouring metal within six months. Today, he says Fabri’s approach also cuts post processing and benefits from the increased flexibility of 3D printing, which allows Fabri to iterate on parts in days.

“What really hurts traditional foundries is they put a design into production, they tool up, they cut a tool, and when they get their first part, they realize something’s wrong,” Davis says. “Then they have to rework it or fix it. They’d like to fix the tooling in the process, but they can’t because they spent so much on the tool and waited six to nine months for it.”

Reinvigorating an industry

Fabri opened its first foundry in Ashland in 2025 and moved production to Billerica in spring of 2026. Davis says the company can go from receiving a design to producing a finished part in as little as a week. Fabri is making parts out of aluminum, copper alloys, and stainless steel, primarily for munitions, aviation, and naval applications. In early 2027, the company plans to begin casting nickel superalloys, the metals used in the turbine blades that can currently have four-year wait times.

The company’s process also enables complex geometries and organic topologies that hard tooling can’t produce.

“We’re building the software that can make these parts more intelligently and faster,” Davis says. “Qualifying for aerospace production with a company like Raytheon is a really big deal. Most startups and 3D-printing companies we know haven’t done it. The ability for us to go from opening our first facility in 2025 to now going into production with some of the nation’s most critical platforms is a huge milestone. We are going to be making parts that are going to fly in the systems and go into the munitions platforms that the country needs.”

When Andrew Voelsing was 8 years old, he decided he was going to MIT.

He was fascinated by space and dreamed of building spaceships. The Institute, and its well-known aerospace engineering program, seemed like the obvious destination. 

Years later, as a senior studying climate system science and engineering, Voelsing is preparing for a different kind of career: serving as a Navy officer, using his technical training to help the armed forces understand the ocean and atmosphere. 

Even during childhood, military service was never far from his mind. His mother attended the U.S. Naval Academy, and his father participated in the Reserve Officers’ Training Corps (ROTC) at the University of Notre Dame. The two met while serving in the Navy in Japan. Growing up, Voelsing heard their stories about military life and saw how their experiences as naval officers shaped their lives.

Voelsing was eventually accepted to the Naval Academy himself. But he chose MIT for the variety of faculty and activities he could pursue while also earning his commission as a Navy officer through the NROTC program. He will soon assume the highest-ranking student role of Battalion Commander, responsible for NROTC across Greater Boston.

But the path from an 8-year-old’s dream to his senior year in Cambridge, Massachusetts, was not quite as straightforward as he had imagined. After arriving at MIT, Voelsing struggled to find his footing academically.

“I thought I was smart in high school. I thought I was at the top of my class, and then I got here and I was failing midterms,” he says. “All my friends seemed to be getting things faster than I was, and I just felt really out of place.”

During the winter break of his first year, he talked with his father about how he was feeling. His father encouraged him to find something that combined what he was good at, what fulfilled him, and what the world needed.

The conversation helped Voelsing reconsider his academic path. He switched from aerospace engineering to climate system science and engineering (Course 1-12), a then-new major offered jointly by the departments of Civil and Environmental Engineering and of Earth, Atmospheric and Planetary Sciences, and returned to campus feeling energized by his studies. 

The change also helped him see how his education could serve a purpose beyond earning a degree. As he continued his ROTC training, he began to consider how technical knowledge about the environment could inform decisions with real-world significance.

The change, he says, was like night and day.

“I really figured out how to study,” he says. “Finding out my own vision of self and academic fulfillment helped me propel myself forward.”

A course in environmental history with Professor Susan Solomon, an atmospheric chemist who helped discover the cause of the Antarctic ozone hole, further shaped his interests. Each week, the class examined a different environmental issue, encouraging students to investigate primary sources and identify questions that had received less attention.

For his final paper, Voelsing researched an agroforestry project in Uganda. The project sparked a deeper interest in global agricultural and food systems, an area he hopes to explore in graduate school.

“There’s not going to be this one technology that comes along, and suddenly climate change isn’t an issue,” he says. “It will be a combination of 100 things making it 1 percent better for us to get across that finish line.”

Outside the classroom, one of Voelsing’s most formative experiences has come through Camp Kesem, a student-run nonprofit that provides free summer camps for children whose parents have been affected by cancer.

He joined MIT’s chapter as a first-year student. The chapter serves more than 200 campers, ages 5 to 18, giving them a week of sleepaway camp filled with activities such as ziplining, swimming, and games.

Over the years, he has taken on several leadership roles. He started as a counselor, then became operations coordinator, helping plan and run the camp. Later, he led alumni relations and organized a fundraising gala. As a senior, he returned to counseling, realizing that the part of the experience he valued most was spending time with the children.

“The thing I enjoy most isn’t all the planning; it’s not all the coordinating,” he says. “The thing I enjoy most is just spending time with kids.”

Camp Kesem also changed how he understands empathy. Before joining, he says, he was uncomfortable when other people cried. Through conversations with campers and fellow counselors, he learned the importance of sitting with someone through difficult emotions instead of trying to make those feelings go away.

“Being able to sit with a person as they grieve and experience that grief with them really changes your views on empathy and changes your views on what is actually important,” he says.

Voelsing has tried to bring that openness to his friendships on campus, too. He values conversations that allow people to understand how different experiences have shaped their perspectives, and he makes an effort to connect people who might not otherwise meet.

“I would give up a good night’s sleep seven nights a week for a good conversation or staying up talking to a friend,” he says. “And I often do.”

That same willingness to learn from others has informed his experience in ROTC. Through the program’s training and relationships with fellow midshipmen, Voelsing has gained a perspective on military service that extends beyond the family stories he grew up hearing.

He recently received his service assignment as a meteorology and oceanography officer, a specialty that will allow him to draw on his scientific background. 

Before beginning his service, however, Voelsing hopes to attend graduate school. He wants to deepen his expertise before joining the Navy full time, believing that further study will allow him to contribute more once he begins his career.

Balancing his academic work, ROTC commitments, and extracurricular activities requires him to be deliberate about how he spends his time. Rather than getting overwhelmed by everything on his plate, Voelsing tries to focus on the responsibility in front of him.   

“I’m trying to tackle it with enthusiasm, and trying to be where my feet are and do the thing well,” he says.

Looking back on his four years at MIT, Voelsing says the most important influence on him has not been a particular class or professor, but the community he has found among his friends. He values the way MIT brings together people with different interests, backgrounds, and ways of thinking, and he hopes to be someone who helps those people connect.

That sense of responsibility also informs how he thinks about the future. Whether in climate science or the military, he wants to approach difficult decisions with an awareness of their consequences and a willingness to take on challenges others might find daunting.

“Somebody’s got to do it. Why not me?” he says. “I think that is really the through line of what I want to do academically and the kind of life I want to lead.”

It was a landmark in history: In 1957, Ghana became the first sub-Saharan African state to achieve independence from colonial rule, having previously been under the control of Great Britain. 

But gaining political independence does not mean the past is left behind, and there has long been public debate about the many long-term effects of the colonial period on state and society in subsequent decades.  

Now, an empirically rich study explores one aspect of the Ghanaian state, the composition of its bureaucracy, during and after British rule. The study finds that two ethnic groups, the Ga and the Fante, which made up 14 percent of the population combined, held over 70 percent of public-sector positions in the 1950s, suggesting goods such as desirable jobs had been heavily distributed along group lines. 

“At the moment of independence, you have a state that is not meritocratic, and is highly captured by certain ethnic groups,” says MIT scholar Noah Nathan, co-author of a newly published paper detailing the study’s findings. As a result, he adds, “You can imagine the politics that creates, which are demands to equalize access to state power and state authority. And things start off on this foot of ethnic political competition over the state. That is in part a legacy of colonial state-building.”

At least in this case, “Independence in Africa wasn’t this hard break from the past,” suggests Nathan, a Ford International Professor of Political Science in MIT’s Department of Political Science. “Africans became the leaders of their countries, but the state institutions that they inherited weren’t born fresh at independence. They were carried over from the colonial period.”

The paper, “The Seeds of State Capture: Merit and Patrimonialism in the Colonial Bureaucracy,” is published in advance online form in the American Political Science Review. Nathan’s co-authors are Sarah Brierley, an associate professor at the London School of Economics and Political Science; George Kwaku Ofoso, an associate professor at the London School of Economics and Political Science; and Tingxuan Zhu PhD ’26, a recent graduate of MIT’s doctoral program in political science at MIT. 

Analyzing names

To conduct the study, the scholars dug into records from the period of British rule in Ghana, which extended from the 19th century until 1957. Using documents from the British National Archives, they examined individual-level employment records for 11,702 bureaucrats from the colonial-era civil service, building a dataset spanning from 1897 to 1955. The records included 6,879 Ghanaian civil servants and 4,805 European civil servants. The scholars’ aim was to shed light on the dynamics of civil service hiring and politics. 

British leaders at the time, and some commentators subsequently, have cast the colonial state as one built on rational, merit-based principles — what scholars call a “Weberian” bureaucracy, named after the famous German sociologist Max Weber. Scholars say that other types of bureaucracies, in contrast, display “patrimonialism,” with jobs being assigned not on merit but through patronage.

To analyze this question further, the scholars looked at 13.8 million names of registered voters from contemporary Ghana, along with localized ethnicity data from the 2010 national census. They used this to establish a baseline of the relationship between names and ethnicity in the country. Then, the scholars went back to the historical archives, and, using the names there, determined the ethnicities of past Ghanaian civil servants, from 1897 to 1955. 

Ultimately this approach showed that on the eve of independence, the Ga and Fante had managed to gain a percentage of civil service jobs that was five times greater than the percentage of the population they represented. That strongly suggests that the colonial-era bureaucracy built under British rule was not merit-based, at least in terms of overall composition. 

“Scholars have said this post-1957 independent Ghana brings with it the introduction of patrimonialism into the bureaucracy, and we’re saying this was going on long before,” Nathan explains. “It actually wasn’t meritocratic at all, and Kwame Nkrumah, the first president of Ghana, didn’t inherit some Weberian rational bureaucracy that he then destroyed. He inherited this thing rife with patrimonialism and patronage, that he then adapted. And that’s a very different interpretation of the post-independence period.”

Exams and education

To further examine the question of merit, the scholars even obtained individual-level results from Ghana’s civil service exams, then analyzed those in relation to which applicants got hired — or passed over for jobs. The results indicate little correspondence between exam results and job offers, again suggesting that ethnicity and social ties mattered more in hiring, on aggregate. 

“If you were from the right ethnic group, you were going to get a job, even if you did pretty poorly on the civil service exam,” Nathan says. “And people from disfavored ethnic groups who did much better on the exam were not getting the same positions.”

The study also rules out a seemingly logical explanation for at least part of the ethnic skew in the composition of the Ghanaian civil service — that hiring reflected levels of educational attainment, even if access to schooling itself might have reflected ethnic iniquities. But the data indicate that was not a factor. 

The scholars located every school in the country geographically, dating to the 1890s, and closely measured access to education for all ethnic groups over time. The Ga and Fante groups may have had some initial advantages in this regard, but by the 1930s, many other groups had caught up in educational attainment and still were not procuring the same relative numbers of civil services jobs through the 1950s. 

“At least in this case, that can’t explain why these two groups got favored in the hiring process,” Nathan says. “It wasn’t just a direct outcome of the fact that some parts of society had more education than others.”

As it happens, geographical proximity to civil service jobs is also not an explanatory factor; the scholars also examined the locations of government offices and found they cannot account for the quantity of jobs going to the Ga and Fante. 

Out of sight

Instead, the researchers believe the likeliest scenario is that, once established in the civil service, people from the Ga and Fante managed to use their social connections to get more and more jobs offered to others from their own groups. Moreover, they may have done so not necessarily with explicit British approval, but with something more resembling indifference on the part of the colonial rulers. 

“The mechanics of the hiring process were very decentralized,” Nathan says. “And the British didn’t know very much about the society they were governing. That created an information asymmetry where local bureaucrats could get friends into jobs in a way their superiors were either not aware of or didn’t have very much incentive to stop.”

All told, Nathan observes, “The ministry of finance in Ghana in 1957 isn’t some new thing that’s created from scratch. It’s the colonial ministry of finance that’s been in place for 60 years, and the bureaucrats in it showed up to work the next day in the same jobs. [The new leaders] inherited thousands of employees who had gotten there through this process.”

The study is one of several that Nathan has published using data to answer questions that span both political science and history. Quantifying the issue in a granular way helps illuminate the large-scale political dynamics.

“People have written about this in broad strokes, even the fact that certain ethnic groups in some countries got more access to positions than others, but they haven’t had detailed microdata on exactly who got which job and when,” Nathan says. “That’s the innovation of our paper … we could unpack the nuts and bolts of how this hiring process happened, and in doing so dispel some conventional wisdom about how African states were built in the colonial period.”

The new paper is not the only piece of scholarship taking this kind of data-intensive approach to studying colonial state-building and its legacy. Ada Johnson-Kanu, an assistant professor of political science at the University of Kentucky, has conducted this sort of study of Nigeria’s bureaucracy over time, in work she completed in 2021. 

Nathan, for his part, says he hopes that other scholars can expand upon this kind of data-driven approach to digging into certain historical questions. 

“There is just incredibly rich archival evidence out there in the world for a lot of countries,” Nathan observes. “I think this is a really fun way to do research.”

Nature Climate Change, Published online: 10 November 2026; doi:10.1038/s41558-026-02769-9

Data centres are among the world’s fastest growing electricity consumers. An analysis of more than 9,000 facilities worldwide links the artificial intelligence-era expansion of data centres to delayed coal plant retirements and around 2 Gt of additional carbon dioxide emissions.

Nature Climate Change, Published online: 10 November 2026; doi:10.1038/s41558-026-02789-5

Scientific grounds for a net-zero policy assume near-zero committed warming over a few decades after CO2 emissions cease. A modelling study suggests that if prior peak temperatures rise above 2 °C before cessation of CO2 emissions, committed warming increases, making it harder to stabilize global warming.

Nature Climate Change, Published online: 10 November 2026; doi:10.1038/s41558-026-02794-8

Data centre expansion may slow the coal-fired power plants phase-out, complicating decarbonization pathways. This study finds that coal units exposed to post-2020 data centres have lower retirement risks and their potential lifetime extensions could cause large additional CO2 emissions.

Nature Climate Change, Published online: 10 November 2026; doi:10.1038/s41558-026-02821-8

This work projects a decline in global emissions in 2026 as a result of clean energy and in response to conflicts. Energy demand has increased, and will continue to, with electric vehicles and solar and wind deployment mitigating emissions; accelerated deployment is needed to reduce emissions.

Today, along with the ACLU, EFF filed an amicus brief in the Sixth Circuit court of appeals in United States v. Sturdivant, a case where police warrantlessly tracked a suspect’s movements using Automated License Plate Reader (ALPR) data. Our brief details the vast, interconnected network of ALPRs that constantly track drivers around the country and argues that this tracking is a “search” under the meaning of the Fourth Amendment.

Sturdivant involves a series of robberies in and around Cleveland, Ohio. Using surveillance camera footage, police identified the make, model and color of the robber’s car, but they did not know its license plate number. After running an open-ended search of two ALPR databases, Flock and ELSAG, officers learned a car belonging to the defendant had been tracked near sites of the robberies, eventually leading to his arrest. Faced with a motion to suppress the ALPR evidence, the district court held that Mr. Sturdivant had no reasonable expectation of privacy in his license plate number because it could be observed by members of the public, and that therefore he had no Fourth Amendment interest in not having his car tracked in public places.

But ALPRs are simply not comparable to observing an individual license plate on a passing car. As we’ve explained in many amicus briefs and in our lawsuits directly challenging ALPR use in San Jose, California and Wisconsin, ALPRs give law enforcement “a virtual time machine to view suspects’, or anyone else’s, past movements (and much more) with just the click of a button.” Just last month, a federal district court agreed and held that ALPRs are a “form of indiscriminate mass surveillance” that can violate the Fourth Amendment.

We hope the Sixth Circuit will reverse the lower court in this case and hold that ALPR searches require a warrant.

Related Cases: Wisconsin Council of Churches v. Milwaukee, Racine, Kenosha & Green BaySIREN and CAIR-CA v. San Jose

The number of petitions being filed at the Patent Trial and Appeal Board (PTAB) has collapsed. That’s not reason for celebration. The PTAB is an important line of defense against low-quality software patents being used in lawsuits against people and companies.

The drop didn’t happen because suddenly patents have become higher quality. It’s simply because USPTO leadership is increasingly refusing to consider good challenges to patents, for reasons unrelated to the merits. The process has become so unreliable that many would-be patent challengers aren’t even trying.

Inter partes review (IPR) is a process that allows people to challenge patents at the Patent Office, rather than having to fight an expensive lawsuit in federal court. It’s one of the most important ways to get bad patents out of the system.

In the third quarter of 2026, July through September, only 97 IPR petitions were filed at the PTAB, compared to 327 petitions during the same period in 2025. Similarly, only 37 IPR petitions were filed in September 2026, compared to 129 petitions in December 2024—the last full month before the current USPTO leadership began reshaping how the Board handles patent challenges.

This dramatic decline shouldn’t just concern patent lawyers. EFF has fought bad patents because so many software and internet patents make claims to the basic tools of free expression online, like sending email, using online ads, or publishing web pages. When bad patents go unchallenged, they affect everyone who cares about the freedom to write new code and make new technology.

That’s why EFF has defended IPR since it came on the scene more than a decade ago. It provides a faster and less expensive way to challenge patents that should never have been granted. Congress created IPRs because the Patent Office makes mistakes. District court litigation is an extraordinarily expensive way to correct them. Without a meaningful administrative review process, bad patents remain powerful weapons in the hands of patent trolls and others seeking to extract settlements from companies that often did nothing wrong.

Now, USPTO leadership is dismantling that safeguard.

Petitions and Institution Rates Are Falling

Since Congress created the IPR patent challenge process in 2012, the PTAB has always had some discretion in deciding which petitions deserve review. But institution rates today are much lower than their typical historical range.

Between fiscal years 2022 and 2024, the PTAB instituted between 66% and 68% of IPR petitions. In fiscal year 2026, which concluded in September 2026, the institution rate fell to 41%. And institution is only the first step of an IPR—it means the PTAB has agreed to hear the challenge, not that the challenger won, and the patent will be invalidated.

That decline helps explain why fewer petitions are being filed in the first place. Preparing an IPR petition takes substantial time and money. While cheaper than federal court, it’s still a complicated administrative process. Before filing, a challenger typically needs to search for prior art, hire experts, and develop detailed technical and legal arguments about why the patent never should have been granted.

Companies were willing to pay for IPRs because, historically, they could expect a predictable process. Petitions that met the statutory standard generally had a reasonable prospect of being heard and decided on the merits. But the USPTO has made it harder to get petitions heard and is throwing them out for reasons that have nothing to do with the merits of the challenges. Faced with these odds, many potential challengers aren’t filing at all.

Even After a Challenge Is Accepted, The USPTO Director Can Cancel It

When the PTAB decides to hear an IPR case, that means the petition has been “instituted”—but that has never meant that the petitioner will definitely win. It means the PTAB has determined the challenge warrants a full merits review, and the parties could expect the proceeding to continue toward a final written decision. But under the current USPTO leadership, that expectation has disappeared. The PTAB’s institution decisions are being repeatedly vacated by the Director.

USPTO Director John Squires has vacated at least 29 decisions granting institution of IPR proceedings, and subsequently denied the petitions, terminating those proceedings before a final written decision. These include cases that PTAB judges had already agreed to hear, as well as proceedings that Squires himself had previously allowed to proceed. This level of retroactive intervention is unprecedented.

In some cases, the parties had already invested substantial time and money litigating before the PTAB, only to have the proceeding terminated based on new reasoning by the Director’s office that arose after institution.

For example, in Revvo Technologies v. Cerebrum Sensor Technologies, the Director vacated an institution decision and remanded the case to the Board. The Board then instituted review again, but the Director reversed that decision. Using a new approach, the Director said that the petition had inconsistencies between claim construction positions taken before the PTAB and in district court. Since then, that decision has been cited as a basis for terminating several other instituted proceedings.

The result is another layer of uncertainty for people who want to challenge bad patents. Getting through the front door of an institution decision is much harder than it used to be—and now, even getting through that door doesn’t mean the PTAB will decide your challenge. Predictability is one of the things that made IPRs work.

Congress Created the PTAB To Avoid Costly Litigation

Congress created IPRs in part because challenging a questionable patent in federal court is enormously expensive and risky. Patent examiners work under serious time constraints, and some patents that shouldn’t have been granted will inevitably issue. Not every patent dispute requires years of expensive district court litigation. Congress recognized that, and created an administrative process, staffed with experts more capable of correcting those mistakes.

That system has benefited the public. Since 2012, it has helped invalidate thousands of patent claims that never should have been issued. The IPR process has protected startups, software developers, nonprofits, and small businesses that otherwise would have faced crushing litigation costs simply for building products or offering services that use everyday internet technology.

EFF supports a balanced IPR system because it should be easier to fight against bogus patents that should never have been issued. The cost of fighting a patent can be a weapon in itself, and weak patents don't just burden large technology companies. Patent trolls routinely target small businesses and independent developers, and have gone after podcasters, retailers, and even coffee shops and nonprofits. These organizations have neither the resources nor the desire to spend years litigating over potentially invalid patents in federal court. IPR offers a chance to challenge those patents before litigation costs get out of control.

If we weaken the IPR system, bad patents become much harder and more expensive to challenge.

We Shouldn't Accept This as the New Normal

Some defenders of the current approach argue that lower institution rates reflect more balance in the patent system. But institution rates at the PTAB have plummeted from 68% to 41% in just two years—a nearly 40% decline. IPR petition filings have reached historic lows. And even proceedings that have already been instituted can now be terminated months later, without a decision on the merits.

These developments send a troubling message to members of the public who might want to use the PTAB: don't bother. The PTAB doesn’t need to institute every petition. But it does need to provide a process that is transparent, predictable, and fair. Right now, confidence in the system is disappearing.

If companies, individuals, and nonprofits conclude that IPRs aren’t worth pursuing, the consequences won't stop at the PTAB. More weak patents that never should have been granted will survive, and patent trolls will use them. That means more defendants facing expensive district court litigation or paying settlements driven by the cost of litigation.

That's not what Congress intended when it created IPRs. It should not become the PTAB's “new normal.”

For the past two decades, the U.S. Supreme Court has been slowly weakening the Alien Tort Statute (ATS), a key human rights law that allows foreign nationals to sue persons or entities in civil court for harms resulting from violations of international law. The court’s recent ruling in Cisco Systems, Inc. v. Doe I dealt the law a final blow, foreclosing—absent new legislation by Congress—any future ability by plaintiffs to use the law to hold technology companies accountable for aiding and abetting human rights abuses. EFF filed multiple amicus briefs in the case, including most recently in the Supreme Court.

The Supreme Court Rejected New Causes of Action Under the ATS

The ATS was passed by the very first Congress in 1789 and in just a few words gives U.S. federal courts jurisdiction to hear civil claims brought by foreign nationals against defendants who violated “the law of nations or a treaty of the United States." There has been a longstanding question about what counts as a valid civil claim under “the law of nations.”

In Sosa v. Alvarez-Machain (2004), the Supreme Court had noted three historical international law claims that the First Congress likely had in mind: violation of safe conducts, infringement of the rights of ambassadors, and piracy, collectively known as the Blackstone Three.

The Sosa court also held that federal courts may recognize other substantive causes of action under the ATS if they reflect international norms that are “specific, universal, and obligatory,” stating that this “door is still ajar subject to vigilant doorkeeping, and thus open to a narrow class of international norms today.”

Thus, the Sosa court recognized that the ATS could be used to bring claims related to historical violations of international law as well as select newer claims reflecting modern concepts of human rights.

In Cisco, the Supreme Court overruled the second Sosa holding. The majority held that the current case, a technology company aiding and abetting torture and other human rights abuses, was beyond the scope of the original three ATS claims and it was not for the court to create new ways of enforcing international law. The court said:

[W]e close the door that Sosa cracked to judicially created ATS liability. We do not disturb Sosa’s holding that the ATS is a jurisdictional statute; nor do we revisit its assumption that causes of action are available for torts corresponding to the Blackstone three. We conclude only that Sosa was overly optimistic in its prediction that there might be a narrow class of cases in which courts may create ATS actions without infringing on the prerogatives of the political branches. In truth, this class is a null set.

What result for this case? Cisco argues that the Ninth Circuit erred in holding that aiding-and-abetting liability exists under the ATS for the torts alleged by the plaintiffs. Cisco is correct. Courts cannot create new rights of action to remedy violations of international law, so there is necessarily no liability for aiding and abetting such violations. Plaintiffs’ ATS claims against Cisco must be dismissed.

Cisco is Off the Hook for Aiding and Abetting Horrific Human Rights Abuses

The Supreme Court’s ending of the plaintiffs’ case against Cisco is hugely disappointing given that the case has been going on since 2011 and the Ninth Circuit in 2023 allowed the case to move forward in an excellent and well-reasoned opinion. The court held, in part:

We conclude that Plaintiffs’ allegations, accepted as true, are sufficient to state a plausible claim that Cisco provided essential technical assistance to the douzheng [violent struggle] of Falun Gong with awareness that the international law violations of torture, arbitrary detention, disappearance, and extrajudicial killing were substantially likely to take place. 

The plaintiffs sued Cisco for the company’s role in building and deploying the “Golden Shield,” also referred to as “The Great Firewall of China”—a vast surveillance system that Cisco began building in the late 1990s and that the company customized to enable the Chinese government to target disfavored minorities, including members of the Falun Gong religion.

The thirteen plaintiffs—Falun Gong practitioners—alleged arrest, detention, and torture, including of themselves and family members, at least one of whom died by beating while being detained. The allegations were horrific: the plaintiffs were placed in forced labor camps, beaten with steel rods, shocked with electric batons, and endured sleep deprivation and violent force-feeding.

Importantly, the plaintiffs also alleged that their private emails, text messages, and other information—intercepted by the Golden Shield—were shown to them and used as part of their torture and forced conversion sessions, which included threats to their family members and others who had communicated with them. 

And now, Cisco is off the hook for all of this.

Congress Must Act to Hold American Tech Companies Accountable for Aiding and Abetting Human Rights Abuses

As we argued in our amicus brief to the Supreme Court, a robust federal statute is necessary given that voluntary and informal mechanisms of corporate human rights accountability are inadequate.

The plaintiffs filed their lawsuit in 2011. The following year, in 2012, Cisco first published its Global Human Rights Policy. Since then, the company has never provided redress to the plaintiffs, despite its currently operative human rights policy stating:

Where we have identified that we have caused or contributed to an adverse human rights impact, we are committed to providing access to and cooperating in remediation for affected individuals, workers, and communities through legitimate processes.

What’s more, the Associated Press conducted an extensive 2025 investigation, which recently won the Pulitzer Prize, further mapping Cisco’s and other American tech companies’ involvement in creating China’s surveillance state over the past three decades. When questioned by the AP reporters, the companies dismissed any notion of human rights obligations and simply “said they adhere to relevant export controls, laws and regulations where they operate.”

In short, Cisco’s commitment to human rights is a fiction.

Given that the U.S. Supreme Court has effectively read the ATS into non-existence, Congress must now step up. While there might be other avenues for redress including under state law, there must also be a comprehensive federal human rights law allowing any plaintiff to hold American companies—especially technology companies—accountable for aiding and abetting human rights abuses, with knowledge or intent that those abuses would occur, and whether the victims suffered those abuses in the U.S. or in foreign countries.

The implications of this gap in U.S. human rights law are significant. As Prof. Harold Koh considered:

[T]he Justices should ask whether affording such immunity in Cisco would implicitly signal to U.S. companies that they could not be held liable for assisting the U.S. government in committing gross violations. Such violations could include constructing a mass surveillance system that may be used to surveil, persecute, retaliate against, and punish real and perceived opponents and constructing systems that autonomously commit war crimes.

American companies should not have a free pass to build out surveillance states (or other technologically sophisticated systems) that not only violate personal privacy or facilitate censorship, but that can also lead to real-world physical violence and even death.

Related Cases: Doe I v. Cisco

Two weeks ago, I attended the [un]prompted AI security conference in San Francisco. It was an excellent event: three days of very dense talks about all aspects of AI security. (My talk was about integrous AI, and I will post it when it is online.)

AI security can be divided into three broad topics. It’s important for researchers to understand which one they are working on. And it’s important for us as a community to work on them all. They are: security of AI, security with AI, and security from AI. [un]prompted had good talks about all three...

Nature Climate Change, Published online: 09 November 2026; doi:10.1038/s41558-026-02797-5

It has been surprisingly difficult to calculate how much warming different regions of the Earth have experienced since pre-industrial times. Now, research shows that in most land regions, the temperature thresholds that governments agreed to in Paris to keep below are already dangerously close.

Nature Climate Change, Published online: 09 November 2026; doi:10.1038/s41558-026-02790-y

Climate targets are set for global temperatures, but what they mean regionally is less clear. Here the authors quantify the amount of human-induced warming in individual IPCC regions and find that, by 2025, they had reached 90–94% (65–76%) of their expected warming levels at the 1.5 °C (2 °C) global warming thresholds.

Today, a scientist who wants to simulate a physical process must pick the right tool for that one job, such as a solver built for flowing water or another designed to model the way a protein folds. Each one is hand-coded around the specific equations that dictate how that process behaves.

Researchers at MIT, the University of British Columbia (UBC), and elsewhere have now replaced that patchwork with a single deep-learning system, called WorldParticle, that could help engineers model different physical phenomena, from sliding sand to folding proteins, with one unified artificial intelligence architecture.

Scientists and engineers could use this system to model processes that typically require separate simulators, such as a fluid sloshing against a deformable solid. This could offer a faster, more accurate way to simulate how sloshing oil might impact an elastic valve in a storage tank, for instance. It could also help engineers study where and how granular materials, like sand or soil, could move when a soft robot touches them.

The system represents every material it simulates, from a dune of sand to a protein molecule, as tiny particles and predicts where those particles will move one step at a time. Because it learns the underlying dynamics rather than being programmed with an equation, the model can adapt to materials, forces, and environments it never encountered during training.  

In the long term, the researchers hope this work helps scientists discover new materials and explore physical processes they would be unable to effectively study with existing simulation methods.

“We want to have a GPT moment for simulation,” says Peter Yichen Chen, a former MIT postdoc who is now an assistant professor at UBC, and is a co-corresponding author of an open-access paper on this technique. “Every time people want to build a simulator for a particular physical phenomenon, they need to code it up from scratch. Our vision is to have a foundation model that works out of the box for any scenario you want to simulate. This is a first step in that direction.”

Chen is joined by co-corresponding and co-lead author Minghao Guo, an MIT postdoc; co-lead authors Caoliwen Wang and Siyuan Chen, UBC graduate students; Wojciech Matusik, an MIT professor of electrical engineering and computer science who leads the Computational Design and Fabrication Group within the Computer Science and Artificial Intelligence Laboratory (CSAIL); and others at UBC, Georgia Tech, Inria, Meta, the University of Pennsylvania, the University of Utah, the University of California at Los Angeles, and the University of Hong Kong. The research will be presented at the ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia.

Unified simulation

For this work, the researchers drew inspiration from world models. These powerful AI systems learn the rules that govern the physical world so they can predict how objects or environments will change over time in response to certain actions or conditions.

The researchers applied these concepts to develop a unified AI architecture that can simulate multiple types of physical phenomena. 

“If you study deformable objects and fluids, you would need two simulators — one for each physical regime. But if you unify them into one model, maybe you could couple the physics of these two processes and gain insights into new problems?” Guo says.

WorldParticle breaks an object into thousands or millions of tiny, three-dimensional dots, or particles, using what is known as a Lagrangian representation. It tracks the movement and velocity of the particles over space and time to simulate the material’s dynamics.

Typically, when scientists model materials and physical processes using Lagrangian particles, they use a governing equation that defines the fundamental laws which control how that material behaves over time and space. They must convert that complex equation into a formula a numerical solver can handle, repeating these steps for each material and process they want to model.

Instead, the researchers designed a neural network that learns to solve governing equations in general, eliminating the need to design a new model and solver to handle each one. 

Once WorldParticle converts an object into particles, a neural network takes the 3D positions and physical properties of the particles as inputs. It outputs their positions and properties for the next frame of the simulation, predicting the material’s motion over time and space in one-second increments. 

“It is a similar architecture to ChatGPT. But instead of predicting one word at a time to write a piece of text, we are predicting how these particles will move, one step at a time,” Chen says.

Before each simulation step, WorldParticle separates the external forces that affect the physical system, like impulse forces that affect an ocean wave when it slams into a seawall. 

After predicting the initial states of the particles, WorldParticle integrates the external forces back into the simulation, correcting its particle predictions based on how those forces affect the object’s behavior. In this way, the system can generalize to unseen materials, changing environmental conditions, and varied forces.

A general, scalable framework

But because a complex object could be broken into millions of particles, the problem quickly becomes too computationally intensive for the model to handle. 

To streamline processing, the researchers employed a merging technique to combine and compress particles while still allowing WorldParticle to capture fine-grained details of their behavior.

“We needed to do a lot of detailed engineering and employ many tweaks to the architecture for it to be truly generalizable and scalable,” Guo says.

They demonstrated WorldParticle by modeling six different physical phenomena, including deformable objects, granular materials, and proteins, with higher accuracy than numerical simulation methods. 

For instance, WorldParticle effectively captured how a granular object would collapse into a pile while some baseline methods left sand particles floating in the air. Their method also modeled how protein structures change over time about 100 times faster than numerical simulators, Guo adds. 

The general-purpose framework could be applied to many materials and processes, including soft robot manipulation tasks, RNA evolution, and astrophysical phenomena like the merging of galaxies, the researchers say.

Moving forward, they want to adapt WorldParticle to simulate these and other physical systems. They also want to build on this work toward the development of a full, general-purpose simulation pipeline for scientific discovery. 

 “We see the unification of computation and science giving us new insights and tools to discover physical phenomena or explain some unexplainable physical phenomena,” Guo says. “With a unified model, we could unlock many discoveries that we can’t even imagine.”

Humans are intuitive by nature. We can sense meaning beyond the literal words that we encounter. Now, a new artificial intelligence-assisted learning platform developed by MIT engineers is enabling robots to read larger meaning into a human’s natural language commands. 

In a study to be presented at the annual Conference on Robot Learning this month, researchers from MIT and New York University introduce FLEX, a flexible feature-editing pipeline for robot motion-planning. 

With FLEX, humans can interact with a robot by giving it instructions in natural, conversational language. The robot can not only follow the instructions literally but also read into the words to draw larger generalizations that help to improve its motion-planning task. 

The team demonstrated FLEX in several simulated scenarios, including one in which a robot serves a cup of coffee. In these virtual scenarios, a person interacts with a robotic arm by typing commands in natural language. As the robot moves the coffee cup over a cluttered table, the person might type “Don’t spill on the laptop!” In response, the robot will reason about why it should avoid the laptop. For this, FLEX incorporates a large language model (LLM) — a type of AI system that is trained on enormous libraries of text, including social media posts, video transcriptions, and web content, which the LLM uses to identify larger trends, patterns, and generalizations. 

“LLMs have seen so many texts from the internet,” says study author Nathan Dennler, a postdoc in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “People might have written about, ‘Oh, my phone dropped in the water and now it doesn’t work,’ or ‘I spilled coffee on my computer and now it doesn’t work.’ So LLMs pick up on the idea that liquids plus devices leads to things not working.”

Through the LLM, the robot reasons that it should also avoid bumping into all electronics. In its updated path, the robot adjusts to avoid both the laptop and a nearby phone, without the person having to explicitly spell this out.

“The idea is that there is some higher-level intention behind the words that you say,” Dennler says. “People naturally get this. But you have to carefully explain that to a robot, which takes time and effort. FLEX gives you an interface of communication with a robot to say, ‘When people say this, they mean that.’”

The study was led by Andreea Bobu, assistant professor of aeronautics and astronautics at MIT. Co-authors are Audrey Lee SM ’26 and Ilia Sucholutsky of New York University.

Beyond what we say

At MIT, Bobu heads up the Collaborative Learning and Autonomy Research (CLEAR) Lab, where her group develops autonomous agents that learn to work for, with, and around people. She is focused on developing assistive and household robots that can carry out physical tasks, and reason and adapt in real-time, in response to a person’s natural, conversational commands. 

“Robots use math to do their trajectory planning. But we can’t expect a consumer to learn to do this math in order to tell a robot what to do,” says Dennler, who is a member of the CLEAR Lab. “Instead of having to get people to learn how to use a robot in a specific way, the goal is to interact with the robot through natural language.”

There is a large effort in the robotics community to incorporate natural language into robotic learning pipelines. Various strategies include using language to clarify a task’s goal, redirect a robot’s trajectory, and update its understanding of what is a safe path to take. These existing methods use natural language to tell the robot how to physically perform a task. But a robot could potentially learn much more from what we tell it. 

Bobu and her colleagues are expanding what robots can learn from natural language commands. In their new study, they focused on enabling a robot to interpret a person’s underlying, unspecified preference from limited natural language commands.  

Absorbing common sense

The team’s new robot learning platform, FLEX, is a three-step process that helps a robot read more into a natural language command, beyond what it is explicitly told to do. 

The first step, “Add,” instructs a robot to add a feature to its list of things in its environment that matter for the task at hand, based on a user’s natural language command. For instance, as a robot is moving a bowling ball from one location to another, a person might interject with “Don’t drop it on my mug!” The robot would then add the mug to a list of things to avoid in its path. This first step is how previous methods have worked. Every time a person makes a correction, the robot adds to its features list, and updates its trajectory to avoid those features. 

“The problem happens when your feature list becomes super long, and some features are no longer relevant because your environment may have changed,” says Dennler, who notes that this approach can grow tiresome if a person has to specify every specific feature to avoid. 

The second step, “Generalize,” is the new solution that FLEX provides. This step incorporates an LLM — in this case, GPT-4o — that considers the mug, along with any other features that the person has so far specified to avoid. The LLM automatically searches through its vast cache of data for patterns and connections among these features, to ultimately make a generalization that it should avoid objects that are fragile, even when the person has not explicitly said so. 

“LLMs have seen so many texts from the internet, from all sorts of people and places,” Dennler says. “Because they’ve trained so much on those things, some of that common sense gets absorbed into the LLM.”

In the third step, “Mask,” the robot’s standard computer vision system assesses its environment to determine what objects are no longer present, and then deletes those objects from its features list. This entire three-step process takes about five seconds. 

The team tested FLEX in simulations of a robot arm carrying out three different tasks: serving a cup of coffee, moving a candle, and carrying a bowling ball. In all three scenarios, volunteers interacted with the virtual robot by typing natural language commands, such as “Don’t spill on my laptop,” “Stay away from the stack of paper,” and “Don’t drop on the mug.” With FLEX, the robot followed all three commands literally and also reasoned at a higher level to not spill on electronics, stay away from flammable objects, and not drop its cargo on fragile things. 

“Everyone’s homes are different, and everyone cares about different things, so no robot comes out of the box knowing what matters to you,” Bobu says. “What excites me about FLEX is that it can pick that up from a sentence or two instead of asking you to spell out every rule — and that’s the difference between a robot people put up with and one they actually want in their home.” 

This work was partly supported by the MIT Generative AI Impact Consortium and the MIT Schwarzman College of Computing’s Social and Ethical Responsibilities of Computing program.

News story: “Scientists discover a bizarre Jurassic ‘squid’ unlike anything ever seen.”

Scientists have identified the “fattest” belemnite ever discovered, a bizarre, barrel-shaped squid relative that lived in Wyoming’s ancient seas 160 million years ago. The exceptionally rare fossils reveal a creature unlike anything previously known, potentially a predator of smaller squid-like animals and a surprising survivor of a supposedly vanished family.

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