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.

The second season of the Netflix reality competition show Squid Game: The Challenge has dropped. (Too many links to pick a few—search for it.)

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

Blog moderation policy.

With global power demand from data centers expected to more than double by 2030, the MIT Energy Initiative (MITEI) in September launched an effort that brings together MIT researchers and industry experts to explore innovative solutions for powering the data-driven future. At its annual research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member companies interested in addressing the challenges of data center power demand. The Data Center Power Forum builds on lessons from MITEI’s May 2025 symposium on the energy to power the expansion of artificial intelligence (AI) and focus panels related to data centers at the fall 2024 research conference.

In the United States, data centers consumed 4 percent of the country’s electricity in 2023, with demand expected to increase to 9 percent by 2030, according to the Electric Power Research Institute. Much of the growth in demand is from the increasing use of AI, which is placing an unprecedented strain on the electric grid. This surge in demand presents a serious challenge for the technology and energy sectors, government policymakers, and everyday consumers, who may see their electric bills skyrocket as a result.

“MITEI has long supported research on ways to produce more efficient and cleaner energy and to manage the electric grid. In recent years, MITEI has also funded dozens of research projects relevant to data center energy issues. Building on this history and knowledge base, MITEI’s Data Center Power Forum is convening a specialized community of industry members who have a vital stake in the sustainable growth of AI and the acceleration of solutions for powering data centers and expanding the grid,” says William H. Green, the director of MITEI and the Hoyt C. Hottel Professor of Chemical Engineering.

MITEI’s mission is to advance zero- and low-carbon solutions to expand energy access and mitigate climate change. MITEI works with companies from across the energy innovation chain, including in the infrastructure, automotive, electric power, energy, natural resources, and insurance sectors. MITEI member companies have expressed strong interest in the Data Center Power Forum and are committing to support focused research on a wide range of energy issues associated with data center expansion, Green says.

MITEI’s Data Center Power Forum will provide its member companies with reliable insights into energy supply, grid load operations and management, the built environment, and electricity market design and regulatory policy for data centers. The forum complements MIT’s deep expertise in adjacent topics such as low-power processors, efficient algorithms, task-specific AI, photonic devices, quantum computing, and the societal consequences of data center expansion. As part of the forum, MITEI’s Future Energy Systems Center is funding projects relevant to data center energy in its upcoming proposal cycles. MITEI Research Scientist Deep Deka has been named the program manager for the forum.

“Figuring out how to meet the power demands of data centers is a complicated challenge. Our research is coming at this from multiple directions, from looking at ways to expand transmission capacity within the electrical grid in order to bring power to where it is needed, to ensuring the quality of electrical service for existing users is not diminished when new data centers come online, and to shifting computing tasks to times and places when and where energy is available on the grid," said Deka.

MITEI currently sponsors substantial research related to data center energy topics across several MIT departments. The existing research portfolio includes more than a dozen projects related to data centers, including low- or zero-carbon solutions for energy supply and infrastructure, electrical grid management, and electricity market policy. MIT researchers funded through MITEI’s industry consortium are also designing more energy-efficient power electronics and processors and investigating behind-the-meter low-/no-carbon power plants and energy storage. MITEI-supported experts are studying how to use AI to optimize electrical distribution and the siting of data centers and conducting techno-economic analyses of data center power schemes. MITEI’s consortium projects are also bringing fresh perspectives to data center cooling challenges and considering policy approaches to balance the interests of shareholders. 

By drawing together industry stakeholders from across the AI and grid value chain, the Data Center Power Forum enables a richer dialog about solutions to power, grid, and carbon management problems in a noncommercial and collaborative setting.

“The opportunity to meet and to hold discussions on key data center challenges with other forum members from different sectors, as well as with MIT faculty members and research scientists, is a unique benefit of this MITEI-led effort,” Green says.

MITEI addressed the issue of data center power needs with its company members during its fall 2024 Annual Research Conference with a panel session titled, “The extreme challenge of powering data centers in a decarbonized way.” MITEI Director of Research Randall Field led a discussion with representatives from large technology companies Google and Microsoft, known as “hyperscalers,” as well as Madrid-based infrastructure developer Ferrovial S.E. and utility company Exelon Corp. Another conference session addressed the related topic, “Energy storage and grid expansion.” This past spring, MITEI focused its annual Spring Symposium on data centers, hosting faculty members and researchers from MIT and other universities, business leaders, and a representative of the Federal Energy Regulatory Commission for a full day of sessions on the topic, “AI and energy: Peril and promise.” 

Over the past few decades, it’s become easier and easier to create fake receipts. Decades ago, it required special paper and printers—I remember a company in the UK advertising its services to people trying to cover up their affairs. Then, receipts became computerized, and faking them required some artistic skills to make the page look realistic.

Now, AI can do it all:

Several receipts shown to the FT by expense management platforms demonstrated the realistic nature of the images, which included wrinkles in paper, detailed itemization that matched real-life menus, and signatures...

The market lets countries fund climate projects and could trigger billions of dollars in investments for cutting emissions.

Dominion Energy spent more than $3 million to try to keep Democrat Jay Jones out of office.

Republicans from Montana to Mississippi have embraced the Biden administration’s push to expand passenger rail nationwide.

The pilot facility planned by Avnos would use direct air capture technology that also pulls moisture from the sky.

The U.K. prime minister is heading to the COP30 climate summit in Brazil to show climate still matters Britain, even in the age of Trump. But back at home, Starmer blows hot and cold on all things green.

Grace Fu said the atmosphere in Belém is “more positive than we had expected.”

A push to slash red tape drives an ever-bigger wedge between Europe’s traditional centrist coalition.

The directive is intended to force companies to provide climate transition plans and exposes them to penalties if they don't address human rights violations in their value chains.

A new delivery particle developed at MIT could make mRNA vaccines more effective and potentially lower the cost per vaccine dose.

In studies in mice, the researchers showed that an mRNA influenza vaccine delivered with their new lipid nanoparticle could generate the same immune response as mRNA delivered by nanoparticles made with FDA-approved materials, but at around 1/100 the dose.

“One of the challenges with mRNA vaccines is the cost,” says Daniel Anderson, a professor in MIT’s Department of Chemical Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research and Institute for Medical Engineering and Science (IMES). “When you think about the cost of making a vaccine that could be distributed widely, it can really add up. Our goal has been to try to make nanoparticles that can give you a safe and effective vaccine response but at a much lower dose.”

While the researchers used their particles to deliver a flu vaccine, they could also be used for vaccines for Covid-19 and other infectious diseases, they say.

Anderson is the senior author of the study, which appears today in Nature Nanotechnology. The lead authors of the paper are Arnab Rudra, a visiting scientist at the Koch Institute; Akash Gupta, a Koch Institute research scientist; and Kaelan Reed, an MIT graduate student.

Efficient delivery

To protect mRNA vaccines from breaking down in the body after injection, they are packaged inside a lipid nanoparticle, or LNP. These fatty spheres help mRNA get into cells so that it can be translated into a fragment of a protein from a pathogen such as influenza or SARS-CoV-2.

In the new study, the MIT team sought to develop particles that can induce an effective immune response, but at a lower dose than the particles now used to deliver Covid-19 mRNA vaccines. That could not only reduce the costs per vaccine dose, but may also help to lessen the potential side effects, the researchers say.

LNPs typically consist of five elements: an ionizable lipid, cholesterol, a helper phospholipid, a polyethylene glycol lipid, and mRNA. In this study, the researchers focused on the ionizable lipid, which plays a key role in vaccine strength.

Based on their knowledge of chemical structures that might improve delivery efficiency, the researchers designed a library of new ionizable lipids. These contained cyclic structures, which can help enhance mRNA delivery, as well as chemical groups called esters, which the researchers believed could also help improve biodegradability.

The researchers then created and screened many combinations of these particle structures in mice to see which could most effectively deliver the gene for luciferase, a bioluminescent protein. Then, they took their top-performing particle and created a library of new variants, which they tested in another round of screening.

From these screens, the top LNP that emerged is one that the researchers called AMG1541. One key feature of these new LNPs is that they are more effective in dealing with a major barrier for delivery particles, known as endosomal escape. After LNPs enter cells, they are isolated in cellular compartments called endosomes, which they need to break out of to deliver their mRNA. The new particles did this more effectively than existing LNPs.

Another advantage of the new LNPs is that the ester groups in the tails make the particles degradable once they have delivered their cargo. This means they can be cleared from the body quickly, which the researchers believe could reduce side effects from the vaccine.

More powerful vaccines

To demonstrate the potential applications of the AMG1541 LNP, the researchers used it to deliver an mRNA influenza vaccine in mice. They compared this vaccine’s effectiveness to a flu vaccine made with a lipid called SM-102, which is FDA-approved and was used by Moderna in its Covid-19 vaccine.

Mice vaccinated with the new particles generated the same antibody response as mice vaccinated with the SM-102 particle, but only 1/100 of the dose was needed to generate that response, the researchers found.

“It’s almost a hundredfold lower dose, but you generate the same amount of antibodies, so that can significantly lower the dose. If it translates to humans, it should significantly lower the cost as well,” Rudra says.

Further experiments revealed that the new LNPs are better able to deliver their cargo to a critical type of immune cells called antigen-presenting cells. These cells chop up foreign antigens and display them on their surfaces, which signals other immune cells such as B and T cells to become activated against that antigen.

The new LNPs are also more likely to accumulate in the lymph nodes, where they encounter many more immune cells.

Using these particles to deliver mRNA flu vaccines could allow vaccine developers to better match the strains of flu that circulate each winter, the researchers say. “With traditional flu vaccines, they have to start being manufactured almost a year ahead of time,” Reed says. “With mRNA, you can start producing it much later in the season and get a more accurate guess of what the circulating strains are going to be, and it may help improve the efficacy of flu vaccines.”

The particles could also be adapted for vaccines for Covid-19, HIV, or any other infectious disease, the researchers say.

“We have found that they work much better than anything that has been reported so far. That’s why, for any intramuscular vaccines, we think that our LNP platforms could be used to develop vaccines for a number of diseases,” Gupta says.

The research was funded by Sanofi, the National Institutes of Health, the Marble Center for Cancer Nanomedicine, and the Koch Institute Support (core) Grant from the National Cancer Institute.

Older buildings let thousands of dollars-worth of energy go to waste each year through leaky roofs, old windows, and insufficient insulation. But even as building owners face mounting pressure to comply with stricter energy codes, making smart decisions about how to invest in efficiency is a major challenge.

Lamarr.AI, born in part from MIT research, is making the process of finding ways to improve the energy efficiency of buildings as easy as clicking a button. When customers order a building review, it triggers a coordinated symphony of drones, thermal and visible-range cameras, and artificial intelligence designed to identify problems and quantify the impact of potential upgrades. Lamarr.AI’s technology also assesses structural conditions, creates detailed 3D models of buildings, and recommends retrofits. The solution is already being used by leading organizations across facilities management as well as by architecture, engineering, and construction firms.

“We identify the root cause of the anomalies we find,” says CEO and co-founder Tarek Rakha PhD ’15. “Our platform doesn’t just say, ‘This is a hot spot and this is a cold spot.’ It specifies ‘This is infiltration or exfiltration. This is missing insulation. This is water intrusion.’ The detected anomalies are also mapped to a 3D model of the building, and there are deeper analytics, such as the cost of each retrofit and the return on investment.”

To date, the company estimates its platform has helped clients across health care, higher education, and multifamily housing avoid over $3 million in unnecessary construction and retrofit costs by recommending targeted interventions over costly full-system replacements, while improving energy performance and extending asset life. For building owners managing portfolios worth hundreds of millions of dollars, Lamarr.AI’s approach represents a fundamental shift from reactive maintenance to strategic asset management.

The founders, who also include MIT Professor John Fernández and Research Scientist Norhan Bayomi SM ’17, PhD ’21, are thrilled to see their technology accelerating the transition to more energy-efficient and higher-performing buildings.

“Reducing carbon emissions in buildings gets you the greatest return on investment in terms of climate interventions, but what has been needed are the technologies and tools to help the real estate and construction sectors make the right decisions in a timely and economical way,” Fernández says.

Automating building scans

Bayomi and Rakha completed their PhDs in the MIT Department of Architecture’s Building Technology Program. For her thesis, Bayomi developed technology to detect features of building exteriors and classify thermal anomalies through scans of buildings, with a specific focus on the impact of heat waves on low-income communities. Bayomi and her collaborators eventually deployed the system to detect air leaks as part of a partnership with a community in New York City.

After graduating MIT, Rakha became an assistant professor at Syracuse University. In 2015, together with fellow Syracuse University Professor Senem Velipasalar, he began developing his concept for drone-based building analytics — an idea that later received support through a grant from New York State’s Department of Economic Development. In 2019, Bayomi and Fernández joined the project, and the team received a $1.8 million research award from the U.S. Department of Energy.

“The technology is like giving a building an MRI using drones, infrared imaging, visible light imaging, and proprietary AI that we developed through computer vision technology, along with large language models for report generation,” Rakha explains.

“When we started the research, we saw firsthand how vulnerable communities were suffering from inefficient buildings, but couldn’t afford comprehensive diagnostics,” Bayomi says. “We knew that if we could automate this process and reduce costs while improving accuracy, we’d unlock a massive market. Now we’re seeing demand from everyone, from municipal buildings to major institutional portfolios.”

Lamarr.AI was officially founded in 2021 to commercialize the technology, and the founders wasted no time tapping into MIT’s entrepreneurial ecosystem. First, they received a small seed grant from the MIT Sandbox Innovation Fund. In 2022, they won the MITdesignX prize and were semifinalists in the MIT $100K Entrepreneurship Competition. The founders named the company after Hedy Lamarr, the famous actress and inventor of a patented technology that became the basis for many modern secure communications.

Current methods for detecting air leaks in buildings utilize fan pressurizers or smoke. Contractors or building engineers may also spot-check buildings with handheld infrared cameras to manually identify temperature differences across individual walls, windows, and ductwork.

Lamarr.AI’s system can perform building inspections far more quickly. Building managers can order the company’s scans online and select when they’d like the drone to fly. Lamarr.AI partners with drone companies worldwide to fly off-the-shelf drones around buildings, providing them with flight plans and specifications for success. Images are then uploaded onto Lamarr.AI’s platform for automated analysis.

“As an example, a survey of a 180,000-square-foot building like the MIT Schwarzman College of Computing, which we scanned, produces around 2,000 images,” Fernández says. “For someone to go through those manually would take a couple of weeks. Our models autonomously analyze those images in a few seconds.”

After the analysis, Lamarr.AI’s platform generates a report that includes the suspected root cause of every weak point found, an estimated cost to correct that problem, and its estimated return on investment using advanced building energy simulations.

“We knew if we were able to quickly, inexpensively, and accurately survey the thermal envelope of buildings and understand their performance, we would be addressing a huge need in the real estate, building construction, and built environment sectors,” Fernández explains. “Thermal anomalies are a huge cause of unwanted heat loss, and more than 45 percent of construction defects are tied to envelope failures.”

The ability to operate at scale is especially attractive to building owners and operators, who often manage large portfolios of buildings across multiple campuses.

“We see Lamarr.AI becoming the premier solution for building portfolio diagnostics and prognosis across the globe, where every building can be equipped not just for the climate crisis, but also to minimize energy losses and be more efficient, safer, and sustainable,” Rakha says.

Building science for everyone

Lamarr.AI has worked with building operators across the U.S. as well as in Canada, the United Kingdom, and the United Arab Emirates.

In June, Lamarr.AI partnered with the City of Detroit, with support from Newlab and Michigan Central, to inspect three municipal buildings to identify areas for improvement. Across two of the buildings, the system identified more than 460 problems like insulation gaps and water leaks. The findings were presented in a report that also utilized energy simulations to demonstrate that upgrades, such as window replacements and targeted weatherization, could reduce HVAC energy use by up to 22 percent.

The entire process took a few days. The founders note that it was the first building inspection drone flight to utilize an off-site operator, an approach that further enhances the scalability of their platform. It also helps further reduce costs, which could make building scans available to a broader swath of people around the world.

“We’re democratizing access to very high-value building science expertise that previously cost tens of thousands per audit,” Bayomi says. “Our platform makes advanced diagnostics affordable enough for routine use, not just one-time assessments. The bigger vision is automated, regular building health monitoring that keeps facilities teams informed in real-time, enabling proactive decisions rather than reactive crisis management. When building intelligence becomes continuous and accessible, operators can optimize performance systematically rather than waiting for problems to emerge.”

Nature Climate Change, Published online: 07 November 2025; doi:10.1038/s41558-025-02482-z

Natural patterns of climate variability, such as the Pacific Decadal Oscillation (PDO), strongly influence regional climate. This study shows that anthropogenic warming now has greater influence than the PDO on North Pacific sea surface temperatures, with implications for predictability and impacts.

Adoption of new tools and technologies occurs when users largely perceive them as reliable, accessible, and an improvement over the available methods and workflows for the cost. Five PhD students from the inaugural class of the MIT-IBM Watson AI Lab Summer Program are utilizing state-of-the-art resources, alleviating AI pain points, and creating new features and capabilities to promote AI usefulness and deployment — from learning when to trust a model that predicts another’s accuracy to more effectively reasoning over knowledge bases. Together, the efforts from the students and their mentors form a through-line, where practical and technically rigorous research leads to more dependable and valuable models across domains.

Building probes, routers, new attention mechanisms, synthetic datasets, and program-synthesis pipelines, the students’ work spans safety, inference efficiency, multimodal data, and knowledge-grounded reasoning. Their techniques emphasize scaling and integration, with impact always in sight.

Learning to trust, and when

MIT math graduate student Andrey Bryutkin’s research prioritizes the trustworthiness of models. He seeks out internal structures within problems, such as equations governing a system and conservation laws, to understand how to leverage them to produce more dependable and robust solutions. Armed with this and working with the lab, Bryutkin developed a method to peer into the nature of large learning models (LLMs) behaviors. Together with the lab’s Veronika Thost of IBM Research and Marzyeh Ghassemi — associate professor and the Germeshausen Career Development Professor in the MIT Department of Electrical Engineering and Computer Science (EECS) and a member of the Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems — Bryutkin explored the “uncertainty of uncertainty” of LLMs. 

Classically, tiny feed-forward neural networks two-to-three layers deep, called probes, are trained alongside LLMs and employed to flag untrustworthy answers from the larger model to developers; however, these classifiers can also produce false negatives and only provide point estimates, which don’t offer much information about when the LLM is failing. Investigating safe/unsafe prompts and question-answer tasks, the MIT-IBM team used prompt-label pairs, as well as the hidden states like activation vectors and last tokens from an LLM, to measure gradient scores, sensitivity to prompts, and out-of-distribution data to determine how reliable the probe was and learn areas of data that are difficult to predict. Their method also helps identify potential labeling noise. This is a critical function, as the trustworthiness of AI systems depends entirely on the quality and accuracy of the labeled data they are built upon. More accurate and consistent probes are especially important for domains with critical data in applications like IBM’s Granite Guardian family of models.

Another way to ensure trustworthy responses to queries from an LLM is to augment them with external, trusted knowledge bases to eliminate hallucinations. For structured data, such as social media connections, financial transactions, or corporate databases, knowledge graphs (KG) are natural fits; however, communications between the LLM and KGs often use fixed, multi-agent pipelines that are computationally inefficient and expensive. Addressing this, physics graduate student Jinyeop Song, along with lab researchers Yada Zhu of IBM Research and EECS Associate Professor Julian Shun created a single-agent, multi-turn, reinforcement learning framework that streamlines this process. Here, the group designed an API server hosting Freebase and Wikidata KGs, which consist of general web-based knowledge data, and a LLM agent that issues targeted retrieval actions to fetch pertinent information from the server. Then, through continuous back-and-forth, the agent appends the gathered data from the KGs to the context and responds to the query. Crucially, the system uses reinforcement learning to train itself to deliver answers that strike a balance between accuracy and completeness. The framework pairs an API server with a single reinforcement learning agent to orchestrate data-grounded reasoning with improved accuracy, transparency, efficiency, and transferability.

Spending computation wisely

The timeliness and completeness of a model’s response carry similar weight to the importance of its accuracy. This is especially true for handling long input texts and those where elements, like the subject of a story, evolve over time, so EECS graduate student Songlin Yang is re-engineering what models can handle at each step of inference. Focusing on transformer limitations, like those in LLMs, the lab’s Rameswar Panda of IBM Research and Yoon Kim, the NBX Professor and associate professor in EECS, joined Yang to develop next-generation language model architectures beyond transformers.

Transformers face two key limitations: high computational complexity in long-sequence modeling due to the softmax attention mechanism, and limited expressivity resulting from the weak inductive bias of RoPE (rotary positional encoding). This means that as the input length doubles, the computational cost quadruples. RoPE allows transformers to understand the sequence order of tokens (i.e., words); however, it does not do a good job capturing internal state changes over time, like variable values, and is limited to the sequence lengths seen during training.

To address this, the MIT-IBM team explored theoretically grounded yet hardware-efficient algorithms. As an alternative to softmax attention, they adopted linear attention, reducing the quadratic complexity that limits the feasible sequence length. They also investigated hybrid architectures that combine softmax and linear attention to strike a better balance between computational efficiency and performance.

Increasing expressivity, they replaced RoPE with a dynamic reflective positional encoding based on the Householder transform. This approach enables richer positional interactions for deeper understanding of sequential information, while maintaining fast and efficient computation. The MIT-IBM team’s advancement reduces the need for transformers to break problems into many steps, instead enabling them to handle more complex subproblems with fewer inference tokens.

Visions anew

Visual data contain multitudes that the human brain can quickly parse, internalize, and then imitate. Using vision-language models (VLMs), two graduate students are exploring ways to do this through code.

Over the past two summers and under the advisement of Aude Oliva, MIT director of the MIT-IBM Watson AI Lab and a senior research scientist in the Computer Science and Artificial Intelligence Laboratory; and IBM Research’s Rogerio Feris, Dan Gutfreund, and Leonid Karlinsky (now at Xero), Jovana Kondic of EECS has explored visual document understanding, specifically charts. These contain elements, such as data points, legends, and axes labels, that require optical character recognition and numerical reasoning, which models still struggle with. In order to facilitate the performance on tasks such as these, Kondic’s group set out to create a large, open-source, synthetic chart dataset from code that could be used for training and benchmarking. 

With their prototype, ChartGen, the researchers created a pipeline that passes seed chart images through a VLM, which is prompted to read the chart and generate a Python script that was likely used to create the chart in the first place. The LLM component of the framework then iteratively augments the code from many charts to ultimately produce over 200,000 unique pairs of charts and their codes, spanning nearly 30 chart types, as well as supporting data and annotation like descriptions and question-answer pairs about the charts. The team is further expanding their dataset, helping to enable critical multimodal understanding to data visualizations for enterprise applications like financial and scientific reports, blogs, and more.

Instead of charts, EECS graduate student Leonardo Hernandez Cano has his eyes on digital design, specifically visual texture generation for CAD applications and the goal of discovering efficient ways to enable to capabilities in VLMs. Teaming up with the lab groups led by Armando Solar-Lezama, EECS professor and Distinguished Professor of Computing in the MIT Schwarzman College of Computing, and IBM Research’s Nathan Fulton, Hernandez Cano created a program synthesis system that learns to refine code on its own. The system starts with a texture description given by a user in the form of an image. It then generates an initial Python program, which produces visual textures, and iteratively refines the code with the goal of finding a program that produces a texture that matches the target description, learning to search for new programs from the data that the system itself produces. Through these refinements, the novel program can create visualizations with the desired luminosity, color, iridescence, etc., mimicking real materials.

When viewed together, these projects, and the people behind them, are making a cohesive push toward more robust and practical artificial intelligence. By tackling the core challenges of reliability, efficiency, and multimodal reasoning, the work paves the way for AI systems that are not only more powerful, but also more dependable and cost-effective, for real-world enterprise and scientific applications.

The MIT Living Climate Futures Lab (LCFL) centers the human dimensions of climate change, bringing together expertise from across MIT to address one of the world’s biggest challenges.

The LCFL has three main goals: “addressing how climate change plays out in everyday life, focusing on community-oriented partnerships, and encouraging cross-disciplinary conversations around climate change on campus,” says Chris Walley, the SHASS Dean’s Distinguished Professor of Anthropology and head of MIT’s Anthropology Section. “We think this is a crucial direction for MIT and will make a strong statement about the kind of human-centered, interdisciplinary work needed to tackle this issue.”

Walley is faculty lead of LCFL, working in collaboration with a group of 19 faculty colleagues and researchers. The LCFL began to coalesce in 2022 when MIT faculty and affiliates already working with communities dealing with climate change issues organized a symposium, inviting urban farmers, place-based environmental groups, and others to MIT. Since then, the lab has consolidated the efforts of faculty and affiliates representing disciplines from across the MIT School of Humanities, Arts, and Social Sciences (SHASS) and the Institute.

Amah Edoh, a cultural anthropologist and managing director of LCFL, says the lab’s collaboration with community organizations and development of experiential learning classes aims to bridge the gap that can exist between the classroom and the real world.

“Sometimes we can find ourselves in a bubble where we’re only in conversation with other people from within academia or our own field of practice. There can be a disconnect between what students are learning somewhat abstractly and the ‘real world’ experience of the issues” Edoh says. “By taking up topics from the multidimensional approach that experiential learning makes possible, students learn to take complexity as a given, which can help to foster more critical thinking in them, and inform their future practice in profound ways.”

Edoh points out that the effects of climate change play out in a huge array of areas: health, food security, livelihoods, housing, and governance structures, to name a few.

“The Living Climate Futures Lab supports MIT researchers in developing the long-term collaborations with community partners that are essential to adequately identifying and responding to the challenges that climate change creates in everyday life,” she says.

Manduhai Buyandelger, professor of anthropology and one of the participants in LCFL, developed the class 21A.S01 (Anthro-Engineering: Decarbonization at the Million-Person Scale), which has in turn sparked related classes. The goal is “to merge technological innovation with people-centered environments.” Working closely with residents of Ulaanbaatar, Mongolia, Buyandelger and collaborator Mike Short, the Class of 1941 Professor of Nuclear Science and Engineering, helped develop a molten salt heat bank as a reusable energy source.

“My work with Mike Short on energy and alternative heating in Mongolia helps to cultivate a new generation of creative and socially minded engineers who prioritize people in thinking about technical solutions,” Buyandelger says, adding, “In our course, we collaborate on creating interdisciplinary methods where we fuse anthropological methods with engineering innovations so that we can expand and deepen our approach to mitigate climate change.”

Iselle Barrios ’25, says 21A.S01 was her first anthropology course. She traveled to Mongolia and was able to experience firsthand all the ways in which the air pollution and heating problem was much larger and more complicated than it seemed from MIT’s Cambridge, Massachusetts, campus.

“It was my first exposure to anthropological and STS critiques of science and engineering, as well as international development,” says Barrios, a chemical engineering major. “It fundamentally reshaped the way I see the role of technology and engineers in the broader social context in which they operate. It really helped me learn to think about problems in a more holistic and people-centered way.”

LCFL participant Alvin Harvey, a postdoc in the MIT Media Lab’s Space Enabled Research Group and a citizen of the Navajo Nation, works to incorporate traditional knowledge in engineering and science to “support global stewardship of earth and space ecologies.”

"I envision the Living Climate Futures Lab as a collaborative space that can be an igniter and sustainer of relationships, especially between MIT and those whose have generational and cultural ties to land and space that is being impacted by climate change,” Harvey says. “I think everyone in our lab understands that protecting our climate future is a collective journey."

Kate Brown, the Thomas M. Siebel Distinguished Professor in History of Science, is also a participant in LCFL. Her current interest is urban food sovereignty movements, in which working-class city dwellers used waste to create “the most productive agriculture in recorded human history,” Brown says. While pursuing that work, Brown has developed relationships and worked with urban farmers in Mansfield, Ohio, as well as in Washington and Amsterdam.

Brown and Susan Solomon, the Lee and Geraldine Martin Professor of Environmental Studies and Chemistry, teach a class called STS.055 (Living Dangerously: Environmental Programs from 1900 to Today) that presents the environmental problems and solutions of the 20th century, and how some “solutions” created more problems over time. Brown also plans to teach a class on the history of global food production once she gets access to a small plot of land on campus for a lab site.

“The Living Climate Futures Lab gives us the structure and flexibility to work with communities that are struggling to find solutions to the problems being created by the climate crisis,” says Brown.

Earlier this year, the MIT Human Insight Collaborative (MITHIC) selected the Living Climate Futures Lab as its inaugural Faculty-Driven Initiative (FDI), which comes with a $500,000 seed grant.

MIT Provost Anantha Chandrakasan, co-chair of MITHIC, says the LCFL exemplifies how we can confront the climate crisis by working in true partnership with the communities most affected.

“By combining scientific insight with cultural understanding and lived experience, this initiative brings a deeper dimension to MIT’s climate efforts — one grounded in collaboration, empathy, and real-world impact,” says Chandrakasan.

Agustín Rayo, the Kenan Sahin Dean of SHASS and co-chair of MITHIC, says the LCFL is precisely the type of interdisciplinary collaboration the FDI program was designed to support.

"By bringing together expertise from across MIT, I am confident the Living Climate Futures Lab will make significant contributions in the Institute’s effort to address the climate crisis," says Rayo.

Walley said the seed grant will support a second symposium in 2026 to be co-designed with community groups, a suite of experiential learning classes, workshops, a speaker series, and other programming. Throughout this development phase, the lab will solicit donor support to build it into an ongoing MIT initiative and a leader in the response to climate change.

Superconductors are like the express trains in a metro system. Any electricity that “boards” a superconducting material can zip through it without stopping and losing energy along the way. As such, superconductors are extremely energy efficient, and are used today to power a variety of applications, from MRI machines to particle accelerators.

But these “conventional” superconductors are somewhat limited in terms of uses because they must be brought down to ultra-low temperatures using elaborate cooling systems to keep them in their superconducting state. If superconductors could work at higher, room-like temperatures, they would enable a new world of technologies, from zero-energy-loss power cables and electricity grids to practical quantum computing systems. And so scientists at MIT and elsewhere are studying “unconventional” superconductors — materials that exhibit superconductivity in ways that are different from, and potentially more promising than, today’s superconductors.

In a promising breakthrough, MIT physicists have today reported their observation of new key evidence of unconventional superconductivity in “magic-angle” twisted tri-layer graphene (MATTG) — a material that is made by stacking three atomically-thin sheets of graphene at a specific angle, or twist, that then allows exotic properties to emerge.

MATTG has shown indirect hints of unconventional superconductivity and other strange electronic behavior in the past. The new discovery, reported in the journal Science, offers the most direct confirmation yet that the material exhibits unconventional superconductivity.

In particular, the team was able to measure MATTG’s superconducting gap — a property that describes how resilient a material’s superconducting state is at given temperatures. They found that MATTG’s superconducting gap looks very different from that of the typical superconductor, meaning that the mechanism by which the material becomes superconductive must also be different, and unconventional.

“There are many different mechanisms that can lead to superconductivity in materials,” says study co-lead author Shuwen Sun, a graduate student in MIT’s Department of Physics. “The superconducting gap gives us a clue to what kind of mechanism can lead to things like room-temperature superconductors that will eventually benefit human society.”

The researchers made their discovery using a new experimental platform that allows them to essentially “watch” the superconducting gap, as the superconductivity emerges in two-dimensional materials, in real-time. They plan to apply the platform to further probe MATTG, and to map the superconducting gap in other 2D materials — an effort that could reveal promising candidates for future technologies.

“Understanding one unconventional superconductor very well may trigger our understanding of the rest,” says Pablo Jarillo-Herrero, the Cecil and Ida Green Professor of Physics at MIT and the senior author of the study. “This understanding may guide the design of superconductors that work at room temperature, for example, which is sort of the Holy Grail of the entire field.”

The study’s other co-lead author is Jeong Min Park PhD ’24; Kenji Watanabe and Takashi Taniguchi of the National Institute for Materials Science in Japan are also co-authors.

The ties that bind

Graphene is a material that comprises a single layer of carbon atoms that are linked in a hexagonal pattern resembling chicken wire. A sheet of graphene can be isolated by carefully exfoliating an atom-thin flake from a block of graphite (the same stuff of pencil lead). In the 2010s, theorists predicted that if two graphene layers were stacked at a very special angle, the resulting structure should be capable of exotic electronic behavior.

In 2018, Jarillo-Herrero and his colleagues became the first to produce magic-angle graphene in experiments, and to observe some of its extraordinary properties. That discovery sprouted an entire new field known as “twistronics,” and the study of atomically thin, precisely twisted materials. Jarillo-Herrero’s group has since studied other configurations of magic-angle graphene with two, three, and more layers, as well as stacked and twisted structures of other two-dimensional materials. Their work, along with other groups, have revealed some signatures of unconventional superconductivity in some structures.

Superconductivity is a state that a material can exhibit under certain conditions (usually at very low temperatures). When a material is a superconductor, any electrons that pass through can pair up, rather than repelling and scattering away. When they couple up in what is known as “Cooper pairs,” the electrons can glide through a material without friction, instead of knocking against each other and flying away as lost energy. This pairing up of electrons is what enables superconductivity, though the way in which they are bound can vary.

“In conventional superconductors, the electrons in these pairs are very far away from each other, and weakly bound,” says Park. “But in magic-angle graphene, we could already see signatures that these pairs are very tightly bound, almost like a molecule. There were hints that there is something very different about this material.”

Tunneling through

In their new study, Jarillo-Herrero and his colleagues aimed to directly observe and confirm unconventional superconductivity in a magic-angle graphene structure. To do so, they would have to measure the material’s superconducting gap.

“When a material becomes superconducting, electrons move together as pairs rather than individually, and there’s an energy ‘gap’ that reflects how they’re bound,” Park explains. “The shape and symmetry of that gap tells us the underlying nature of the superconductivity.”

Scientists have measured the superconducting gap in materials using specialized techniques, such as tunneling spectroscopy. The technique takes advantage of a quantum mechanical property known as “tunneling.” At the quantum scale, an electron behaves not just as a particle, but also as a wave; as such, its wave-like properties enable an electron to travel, or “tunnel,” through a material, as if it could move through walls.

Such tunneling spectroscopy measurements can give an idea of how easy it is for an electron to tunnel into a material, and in some sense, how tightly packed and bound the electrons in the material are. When performed in a superconducting state, it can reflect the properties of the superconducting gap. However, tunneling spectroscopy alone cannot always tell whether the material is, in fact, in a superconducting state. Directly linking a tunneling signal to a genuine superconducting gap is both essential and experimentally challenging.

In their new work, Park and her colleagues developed an experimental platform that combines electron tunneling with electrical transport — a technique that is used to gauge a material’s superconductivity, by sending current through and continuously measuring its electrical resistance (zero resistance signals that a material is in a superconducting state).

The team applied the new platform to measure the superconducting gap in MATTG. By combining tunneling and transport measurements in the same device, they could unambiguously identify the superconducting tunneling gap, one that appeared only when the material exhibited zero electrical resistance, which is the hallmark of superconductivity. They then tracked how this gap evolved under varying temperature and magnetic fields. Remarkably, the gap displayed a distinct V-shaped profile, which was clearly different from the flat and uniform shape of conventional superconductors.

This V shape reflects a certain unconventional mechanism by which electrons in MATTG pair up to superconduct. Exactly what that mechanism is remains unknown. But the fact that the shape of the superconducting gap in MATTG stands out from that of the typical superconductor provides key evidence that the material is an unconventional superconductor.

In conventional superconductors, electrons pair up through vibrations of the surrounding atomic lattice, which effectively jostle the particles together. But Park suspects that a different mechanism could be at work in MATTG.

“In this magic-angle graphene system, there are theories explaining that the pairing likely arises from strong electronic interactions rather than lattice vibrations,” she posits. “That means electrons themselves help each other pair up, forming a superconducting state with special symmetry.”

Going forward, the team will test other two-dimensional twisted structures and materials using the new experimental platform.

“This allows us to both identify and study the underlying electronic structures of superconductivity and other quantum phases as they happen, within the same sample,” Park says. “This direct view can reveal how electrons pair and compete with other states, paving the way to design and control new superconductors and quantum materials that could one day power more efficient technologies or quantum computers.”

This research was supported, in part, by the U.S. Army Research Office, the U.S. Air Force Office of Scientific Research, the MIT/MTL Samsung Semiconductor Research Fund, the Sagol WIS-MIT Bridge Program, the National Science Foundation, the Gordon and Betty Moore Foundation, and the Ramon Areces Foundation.

EFF has, for many years, raised the alarm about the proliferation of stalkerware—commercially-available apps designed to be installed covertly on another person’s device and exfiltrate data from that device without their knowledge. In particular, we have urged the makers of anti-virus products for Android phones to improve their detection of stalkerware and call it out explicitly to users when it is found. In 2020  and 2021, AV Comparatives ran tests to see how well the most popular anti-virus products detected stalkerware from many different vendors. The results were mixed, with some high-scoring companies and others that had alarmingly low detection rates. Since malware detection is an endless game of cat and mouse between anti-virus companies and malware developers, we felt that the time was right to take a more up-to-date snapshot of how well the anti-virus companies are performing. We’ve teamed up with the researchers at AV Comparatives to test the most popular anti-virus products for Android to see how well they detect the most popular stalkerware products in 2025.

Here is what we found:

Stalkerware detection is still a mixed bag. Notably, Malwarebytes detected 100% of the stalkerware products we tested for. ESET, Bitdefender, McAfee, and Kaspersky detected all but one sample. This is a marked improvement over the 2021 test, which also found only one app with a 100% detection rate (G Data), but the next-best performing products had detect rates of 80-85%. Google Play Protect and Trend Micro had the lowest detection rates in the 2025 test, at 53% and 59% respectively. The poor performance of Google Play Protect is unsurprising: because it is the anti-virus solution on so many Android phones by default, some stalkerware includes specific instructions to disable detection by Google Play Protect as part of the installation process.

There are fewer stalkerware products out there. In 2020 and 2021, AV Comparatives tested 20 unique stalkerware products from different vendors. In 2025, we tested 17. We found that many stalkerware apps are essentially variations on the same underlying product and that the number of unique underlying products appears to have decreased in recent years. We cannot be certain about the cause of this decline, but we speculate that increased attention from regulators may be a factor. The popularity of small, cheap, Bluetooth-enabled physical trackers such as Apple AirTags and Tiles as an alternative method of location-tracking may also be undercutting the stalkerware market. 

We hope that these tests will help survivors of domestic abuse and others who are concerned about stalkerware on their Android devices make informed choices about their anti-virus apps. We also hope that exposing the gaps that these products have in stalkerware detection will renew interest in this problem at anti-virus companies.

You can find the full results of the test here (PDF). 

The Department of Justice has indicted thirty-one people over the high-tech rigging of high-stakes poker games.

In a typical legitimate poker game, a dealer uses a shuffling machine to shuffle the cards randomly before dealing them to all the players in a particular order. As set forth in the indictment, the rigged games used altered shuffling machines that contained hidden technology allowing the machines to read all the cards in the deck. Because the cards were always dealt in a particular order to the players at the table, the machines could determine which player would have the winning hand. This information was transmitted to an off-site member of the conspiracy, who then transmitted that information via cellphone back to a member of the conspiracy who was playing at the table, referred to as the “Quarterback” or “Driver.” The Quarterback then secretly signaled this information (usually by prearranged signals like touching certain chips or other items on the table) to other co-conspirators playing at the table, who were also participants in the scheme. Collectively, the Quarterback and other players in on the scheme (i.e., the cheating team) used this information to win poker games against unwitting victims, who sometimes lost tens or hundreds of thousands of dollars at a time. The defendants used other cheating technology as well, such as a chip tray analyzer (essentially, a poker chip tray that also secretly read all cards using hidden cameras), an x-ray table that could read cards face down on the table, and special contact lenses or eyeglasses that could read pre-marked cards. ...