Exposing biases, moods, personalities, and abstract concepts hidden in large language models

By now, ChatGPT, Claude, and other large language models have accumulated so much human knowledge that they’re far from simple answer-generators; they can also express abstract concepts, such as certain tones, personalities, biases, and moods. However, it’s not obvious exactly how these models represent abstract concepts to begin with from the knowledge they contain.

Now a team from MIT and the University of California San Diego has developed a way to test whether a large language model (LLM) contains hidden biases, personalities, moods, or other abstract concepts. Their method can zero in on connections within a model that encode for a concept of interest. What’s more, the method can then manipulate, or “steer” these connections, to strengthen or weaken the concept in any answer a model is prompted to give.

The team proved their method could quickly root out and steer more than 500 general concepts in some of the largest LLMs used today. For instance, the researchers could home in on a model’s representations for personalities such as “social influencer” and “conspiracy theorist,” and stances such as “fear of marriage” and “fan of Boston.” They could then tune these representations to enhance or minimize the concepts in any answers that a model generates.

In the case of the “conspiracy theorist” concept, the team successfully identified a representation of this concept within one of the largest vision language models available today. When they enhanced the representation, and then prompted the model to explain the origins of the famous “Blue Marble” image of Earth taken from Apollo 17, the model generated an answer with the tone and perspective of a conspiracy theorist.

The team acknowledges there are risks to extracting certain concepts, which they also illustrate (and caution against). Overall, however, they see the new approach as a way to illuminate hidden concepts and potential vulnerabilities in LLMs, that could then be turned up or down to improve a model’s safety or enhance its performance.

“What this really says about LLMs is that they have these concepts in them, but they’re not all actively exposed,” says Adityanarayanan “Adit” Radhakrishnan, assistant professor of mathematics at MIT. “With our method, there’s ways to extract these different concepts and activate them in ways that prompting cannot give you answers to.”

The team published their findings today in a study appearing in the journal Science. The study’s co-authors include Radhakrishnan, Daniel Beaglehole and Mikhail Belkin of UC San Diego, and Enric Boix-Adserà of the University of Pennsylvania.

A fish in a black box

As use of OpenAI’s ChatGPT, Google’s Gemini, Anthropic’s Claude, and other artificial intelligence assistants has exploded, scientists are racing to understand how models represent certain abstract concepts such as “hallucination” and “deception.” In the context of an LLM, a hallucination is a response that is false or contains misleading information, which the model has “hallucinated,” or constructed erroneously as fact.

To find out whether a concept such as “hallucination” is encoded in an LLM, scientists have often taken an approach of “unsupervised learning” — a type of machine learning in which algorithms broadly trawl through unlabeled representations to find patterns that might relate to a concept such as “hallucination.” But to Radhakrishnan, such an approach can be too broad and computationally expensive.

“It’s like going fishing with a big net, trying to catch one species of fish. You’re gonna get a lot of fish that you have to look through to find the right one,” he says. “Instead, we’re going in with bait for the right species of fish.”

He and his colleagues had previously developed the beginnings of a more targeted approach with a type of predictive modeling algorithm known as a recursive feature machine (RFM). An RFM is designed to directly identify features or patterns within data by leveraging a mathematical mechanism that neural networks — a broad category of AI models that includes LLMs — implicitly use to learn features.

Since the algorithm was an effective, efficient approach for capturing features in general, the team wondered whether they could use it to root out representations of concepts, in LLMs, which are by far the most widely used type of neural network and perhaps the least well-understood.

“We wanted to apply our feature learning algorithms to LLMs to, in a targeted way, discover representations of concepts in these large and complex models,” Radhakrishnan says.

Converging on a concept

The team’s new approach identifies any concept of interest within a LLM and “steers” or guides a model’s response based on this concept. The researchers looked for 512 concepts within five classes: fears (such as of marriage, insects, and even buttons); experts (social influencer, medievalist); moods (boastful, detachedly amused); a preference for locations (Boston, Kuala Lumpur); and personas (Ada Lovelace, Neil deGrasse Tyson).

The researchers then searched for representations of each concept in several of today’s large language and vision models. They did so by training RFMs to recognize numerical patterns in an LLM that could represent a particular concept of interest.

A standard large language model is, broadly, a neural network that takes a natural language prompt, such as “Why is the sky blue?” and divides the prompt into individual words, each of which is encoded mathematically as a list, or vector, of numbers. The model takes these vectors through a series of computational layers, creating matrices of many numbers that, throughout each layer, are used to identify other words that are most likely to be used to respond to the original prompt. Eventually, the layers converge on a set of numbers that is decoded back into text, in the form of a natural language response.

The team’s approach trains RFMs to recognize numerical patterns in an LLM that could be associated with a specific concept. As an example, to see whether an LLM contains any representation of a “conspiracy theorist,” the researchers would first train the algorithm to identify patterns among LLM representations of 100 prompts that are clearly related to conspiracies, and 100 other prompts that are not. In this way, the algorithm would learn patterns associated with the conspiracy theorist concept. Then, the researchers can mathematically modulate the activity of the conspiracy theorist concept by perturbing LLM representations with these identified patterns. 

The method can be applied to search for and manipulate any general concept in an LLM. Among many examples, the researchers identified representations and manipulated an LLM to give answers in the tone and perspective of a “conspiracy theorist.” They also identified and enhanced the concept of “anti-refusal,” and showed that whereas normally, a model would be programmed to refuse certain prompts, it instead answered, for instance giving instructions on how to rob a bank.

Radhakrishnan says the approach can be used to quickly search for and minimize vulnerabilities in LLMs. It can also be used to enhance certain traits, personalities, moods, or preferences, such as emphasizing the concept of “brevity” or “reasoning” in any response an LLM generates. The team has made the method’s underlying code publicly available.

“LLMs clearly have a lot of these abstract concepts stored within them, in some representation,” Radhakrishnan says. “There are ways where, if we understand these representations well enough, we can build highly specialized LLMs that are still safe to use but really effective at certain tasks.”

This work was supported, in part, by the National Science Foundation, the Simons Foundation, the TILOS institute, and the U.S. Office of Naval Research. 

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A neural blueprint for human-like intelligence in soft robots

A new artificial intelligence control system enables soft robotic arms to learn a wide repertoire of motions and tasks once, then adjust to new scenarios on the fly, without needing retraining or sacrificing functionality. 

This breakthrough brings soft robotics closer to human-like adaptability for real-world applications, such as in assistive robotics, rehabilitation robots, and wearable or medical soft robots, by making them more intelligent, versatile, and safe.

The work was led by the Mens, Manus and Machina (M3S) interdisciplinary research group — a play on the Latin MIT motto “mens et manus,” or “mind and hand,” with the addition of “machina” for “machine” — within the Singapore-MIT Alliance for Research and Technology. Co-leading the project are researchers from the National University of Singapore (NUS), alongside collaborators from MIT and Nanyang Technological University in Singapore (NTU Singapore).

Unlike regular robots that move using rigid motors and joints, soft robots are made from flexible materials such as soft rubber and move using special actuators — components that act like artificial muscles to produce physical motion. While their flexibility makes them ideal for delicate or adaptive tasks, controlling soft robots has always been a challenge because their shape changes in unpredictable ways. Real-world environments are often complicated and full of unexpected disturbances, and even small changes in conditions — like a shift in weight, a gust of wind, or a minor hardware fault — can throw off their movements. 

Despite substantial progress in soft robotics, existing approaches often can only achieve one or two of the three capabilities needed for soft robots to operate intelligently in real-world environments: using what they’ve learned from one task to perform a different task, adapting quickly when the situation changes, and guaranteeing that the robot will stay stable and safe while adapting its movements. This lack of adaptability and reliability has been a major barrier to deploying soft robots in real-world applications until now.

In an open-access study titled “A general soft robotic controller inspired by neuronal structural and plastic synapses that adapts to diverse arms, tasks, and perturbations,” published Jan. 6 in Science Advances, the researchers describe how they developed a new AI control system that allows soft robots to adapt across diverse tasks and disturbances. The study takes inspiration from the way the human brain learns and adapts, and was built on extensive research in learning-based robotic control, embodied intelligence, soft robotics, and meta-learning.

The system uses two complementary sets of “synapses” — connections that adjust how the robot moves — working in tandem. The first set, known as “structural synapses”, is trained offline on a variety of foundational movements, such as bending or extending a soft arm smoothly. These form the robot’s built‑in skills and provide a strong, stable foundation. The second set, called “plastic synapses,” continually updates online as the robot operates, fine-tuning the arm’s behavior to respond to what is happening in the moment. A built-in stability measure acts like a safeguard, so even as the robot adjusts during online adaptation, its behavior remains smooth and controlled.

“Soft robots hold immense potential to take on tasks that conventional machines simply cannot, but true adoption requires control systems that are both highly capable and reliably safe. By combining structural learning with real-time adaptiveness, we’ve created a system that can handle the complexity of soft materials in unpredictable environments,” says MIT Professor Daniela Rus, co-lead principal investigator at M3S, director of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-corresponding author of the paper. “It’s a step closer to a future where versatile soft robots can operate safely and intelligently alongside people — in clinics, factories, or everyday lives.”

“This new AI control system is one of the first general soft-robot controllers that can achieve all three key aspects needed for soft robots to be used in society and various industries. It can apply what it learned offline across different tasks, adapt instantly to new conditions, and remain stable throughout — all within one control framework,” says Associate Professor Zhiqiang Tang, first author and co-corresponding author of the paper who was a postdoc at M3S and at NUS when he carried out the research and is now an associate professor at Southeast University in China (SEU China).

The system supports multiple task types, enabling soft robotic arms to execute trajectory tracking, object placement, and whole-body shape regulation within one unified approach. The method also generalizes across different soft-arm platforms, demonstrating cross-platform applicability. 

The system was tested and validated on two physical platforms — a cable-driven soft arm and a shape-memory-alloy–actuated soft arm — and delivered impressive results. It achieved a 44–55 percent reduction in tracking error under heavy disturbances; over 92 percent shape accuracy under payload changes, airflow disturbances, and actuator failures; and stable performance even when up to half of the actuators failed. 

“This work redefines what’s possible in soft robotics. We’ve shifted the paradigm from task-specific tuning and capabilities toward a truly generalizable framework with human-like intelligence. It is a breakthrough that opens the door to scalable, intelligent soft machines capable of operating in real-world environments,” says Professor Cecilia Laschi, co-corresponding author and principal investigator at M3S, Provost’s Chair Professor in the NUS Department of Mechanical Engineering at the College of Design and Engineering, and director of the NUS Advanced Robotics Centre.

This breakthrough opens doors for more robust soft robotic systems to develop manufacturing, logistics, inspection, and medical robotics without the need for constant reprogramming — reducing downtime and costs. In health care, assistive and rehabilitation devices can automatically tailor their movements to a patient’s changing strength or posture, while wearable or medical soft robots can respond more sensitively to individual needs, improving safety and patient outcomes.

The researchers plan to extend this technology to robotic systems or components that can operate at higher speeds and more complex environments, with potential applications in assistive robotics, medical devices, and industrial soft manipulators, as well as integration into real-world autonomous systems.

The research conducted at SMART was supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise program.

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Parking-aware navigation system could prevent frustration and emissions

It happens every day — a motorist heading across town checks a navigation app to see how long the trip will take, but they find no parking spots available when they reach their destination. By the time they finally park and walk to their destination, they’re significantly later than they expected to be.

Most popular navigation systems send drivers to a location without considering the extra time that could be needed to find parking. This causes more than just a headache for drivers. It can worsen congestion and increase emissions by causing motorists to cruise around looking for a parking spot. This underestimation could also discourage people from taking mass transit because they don’t realize it might be faster than driving and parking.

MIT researchers tackled this problem by developing a system that can be used to identify parking lots that offer the best balance of proximity to the desired location and likelihood of parking availability. Their adaptable method points users to the ideal parking area rather than their destination.

In simulated tests with real-world traffic data from Seattle, this technique achieved time savings of up to 66 percent in the most congested settings. For a motorist, this would reduce travel time by about 35 minutes, compared to waiting for a spot to open in the closest parking lot.

While they haven’t designed a system ready for the real world yet, their demonstrations show the viability of this approach and indicate how it could be implemented.

“This frustration is real and felt by a lot of people, and the bigger issue here is that systematically underestimating these drive times prevents people from making informed choices. It makes it that much harder for people to make shifts to public transit, bikes, or alternative forms of transportation,” says MIT graduate student Cameron Hickert, lead author on a paper describing the work.

Hickert is joined on the paper by Sirui Li PhD ’25; Zhengbing He, a research scientist in the Laboratory for Information and Decision Systems (LIDS); and senior author Cathy Wu, the Class of 1954 Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS) at MIT, and a member of LIDS. The research appears today in Transactions on Intelligent Transportation Systems.

Probable parking

To solve the parking problem, the researchers developed a probability-aware approach that considers all possible public parking lots near a destination, the distance to drive there from a point of origin, the distance to walk from each lot to the destination, and the likelihood of parking success.

The approach, based on dynamic programming, works backward from good outcomes to calculate the best route for the user.

Their method also considers the case where a user arrives at the ideal parking lot but can’t find a space. It takes into the account the distance to other parking lots and the probability of success of parking at each.

“If there are several lots nearby that have slightly lower probabilities of success, but are very close to each other, it might be a smarter play to drive there rather than going to the higher-probability lot and hoping to find an opening. Our framework can account for that,” Hickert says.

In the end, their system can identify the optimal lot that has the lowest expected time required to drive, park, and walk to the destination.

But no motorist expects to be the only one trying to park in a busy city center. So, this method also incorporates the actions of other drivers, which affect the user’s probability of parking success.

For instance, another driver may arrive at the user’s ideal lot first and take the last parking spot. Or another motorist could try parking in another lot but then park in the user’s ideal lot if unsuccessful. In addition, another motorist may park in a different lot and cause spillover effects that lower the user’s chances of success.

“With our framework, we show how you can model all those scenarios in a very clean and principled manner,” Hickert says.

Crowdsourced parking data

The data on parking availability could come from several sources. For example, some parking lots have magnetic detectors or gates that track the number of cars entering and exiting.

But such sensors aren’t widely used, so to make their system more feasible for real-world deployment, the researchers studied the effectiveness of using crowdsourced data instead.

For instance, users could indicate available parking using an app. Data could also be gathered by tracking the number of vehicles circling to find parking, or how many enter a lot and exit after being unsuccessful.

Someday, autonomous vehicles could even report on open parking spots they drive by.

“Right now, a lot of that information goes nowhere. But if we could capture it, even by having someone simply tap ‘no parking’ in an app, that could be an important source of information that allows people to make more informed decisions,” Hickert adds.

The researchers evaluated their system using real-world traffic data from the Seattle area, simulating different times of day in a congested urban setting and a suburban area. In congested settings, their approach cut total travel time by about 60 percent compared to sitting and waiting for a spot to open, and by about 20 percent compared to a strategy of continually driving to the next closet parking lot.

They also found that crowdsourced observations of parking availability would have an error rate of only about 7 percent, compared to actual parking availability. This indicates it could be an effective way to gather parking probability data.

In the future, the researchers want to conduct larger studies using real-time route information in an entire city. They also want to explore additional avenues for gathering data on parking availability, such as using satellite images, and estimate potential emissions reductions.

“Transportation systems are so large and complex that they are really hard to change. What we look for, and what we found with this approach, is small changes that can have a big impact to help people make better choices, reduce congestion, and reduce emissions,” says Wu.

This research was supported, in part, by Cintra, the MIT Energy Initiative, and the National Science Foundation.

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How MIT OpenCourseWare is fueling one learner’s passion for education

Training for a clerical military role in France, Gustavo Barboza felt a spark he couldn’t ignore. He remembered his love of learning, which once guided him through two college semesters of mechanical engineering courses in his native Colombia, coupled with supplemental resources from MIT Open Learning’s OpenCourseWare. Now, thousands of miles away, he realized it was time to follow that spark again.

“I wasn’t ready to sit down in the classroom,” says Barboza, remembering his initial foray into higher education. “I left to try and figure out life. I realized I wanted more adventure.”

Joining the military in France in 2017 was his answer. For the first three years of service, he was very military-minded, only focused on his training and deployments. With more seniority, he took on more responsibilities, and eventually was sent to take a four-month training course on military correspondence and software. 

“I reminded myself that I like to study,” he says. “I started to go back to OpenCourseWare because I knew in the back of my mind that these very complete courses were out there.”

At that point, Barboza realized that military service was only a chapter in his life, and the next would lead him back to learning. He was still interested in engineering, and knew that MIT OpenCourseWare could help prepare him for what was next. 

He dove into OpenCourseWare’s free, online, open educational resources — which cover nearly the entire MIT curriculum — including classical mechanics, intro to electrical engineering, and single variable calculus with David Jerison, which he says was his most-visited resource. These allowed him to brush up on old skills and learn new ones, helping him tremendously in preparing for college entrance exams and his first-year courses. 

Now in his third year at Grenoble-Alpes University, Barboza studies electrical engineering, a shift from his initial interest in mechanical engineering.

“There is an OpenCourseWare lecture that explains all the specializations you can get into with electrical engineering,” he says. “They go from very natural things to things like microprocessors. What interests me is that if someone says they are an electrical engineer, there are so many different things they could be doing.” 

At this point in his academic career, Barboza is most interested in microelectronics and the study of radio frequencies and electromagnetic waves. But he admits he has more to learn and is open to where his studies may take him. 

MIT OpenCourseWare remains a valuable resource, he says. When thinking about his future, he checks out graduate course listings and considers the different paths he might take. When he is having trouble with a certain concept, he looks for a lecture on the subject, undeterred by the differences between French and U.S. conventions.  

“Of course, the science doesn't change, but the way you would write an equation or draw a circuit is different at my school in France versus what I see from MIT. So, you have to be careful,” he explains. “But it is still the first place I visit for problem sets, readings, and lecture notes. It’s amazing.”

The thoroughness and openness of MIT Open Learning’s courses and resources — like OpenCourseWare — stand out to Barboza. In the wide world of the internet, he has found resources from other universities, but he says their offerings are not as robust. And in a time of disinformation and questionable sources, he appreciates that MIT values transparency, accessibility, and knowledge. 

“Human knowledge has never been more accessible,” he says. “MIT puts coursework online and says, ‘here’s what we do.’ As long as you have an internet connection, you can learn all of it.”

“I just feel like MIT OpenCourseWare is what the internet was originally for,” Barboza continues. “A network for sharing knowledge. I’m a big fan.”

Explore lifelong learning opportunities from MIT, including courses, resources, and professional programs, on MIT Learn.

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3D-printing platform rapidly produces complex electric machines

A broken motor in an automated machine can bring production on a busy factory floor to a halt. If engineers can’t find a replacement part, they may have to order one from a distributor hundreds of miles away, leading to costly production delays.

It would be easier, faster, and cheaper to make a new motor onsite, but fabricating electric machines typically requires specialized equipment and complicated processes, which restricts production to a few manufacturing centers.

In an effort to democratize the manufacturing of complex devices, MIT researchers have developed a multimaterial 3D-printing platform that could be used to fully print electric machines in a single step.

They designed their system to process multiple functional materials, including electrically conductive materials and magnetic materials, using four extrusion tools that can handle varied forms of printable material. The printer switches between extruders, which deposit material by squeezing it through a nozzle as it fabricates a device one layer at a time.

The researchers used this system to produce a fully 3D-printed electric linear motor in a matter of hours using five materials. They only needed to perform one post-processing step for the motor to be fully functional.

The assembled device performed as well or better than similar motors that require more complex fabrication methods or additional post-processing steps.

In the long run, this 3D printing platform could be used to rapidly fabricate customizable electronic components for robots, vehicles, or medical equipment with much less waste.

“This is a great feat, but it is just the beginning. We have an opportunity to fundamentally change the way things are made by making hardware onsite in one step, rather than relying on a global supply chain. With this demonstration, we’ve shown that this is feasible,” says Luis Fernando Velásquez-García, a principal research scientist in MIT’s Microsystems Technology Laboratories (MTL) and senior author of a paper describing the 3D-printing platform, which appears today in Virtual and Physical Prototyping.

He is joined on the paper by electrical engineering and computer science (EECS) graduate students Jorge Cañada, who is the lead author, and Zoey Bigelow.

More materials

The researchers focused on extrusion 3D printing, a tried-and-true method that involves squirting material through a nozzle to fabricate an object one layer at a time.

To fabricate an electric machine, the researchers needed to be able to switch between multiple materials that offer different functionalities. For instance, the device would need an electrically conductive material to carry electric current and hard magnetic materials to generate magnetic fields for efficient energy conversion.

Most multimaterial extrusion 3D printing systems can only switch between two materials that come in the same form, such as filament or pellets, so the researchers had to design their own. They retrofit an existing printer with four extruders that can each handle a different form of feedstock.

They carefully designed each extruder to balance the requirements and limitations of the material. For instance, the electrically conductive material must be able to harden without the use of too much heat or UV light because this can degrade the dielectric material.

At the same time, the best-performing electrically conductive materials come in the form of inks which are extruded using a pressure system. This process has vastly different requirements than standard extruders that use heated nozzles to squirt melted filament or pellets.

“There were significant engineering challenges. We had to figure out how to marry together many different expressions of the same printing method — extrusion — seamlessly into one platform,” Velásquez-García says.

The researchers utilized strategically placed sensors and a novel control framework so each tool is picked up and put down consistently by the platform’s robotic arms, and so each nozzle moves precisely and predictably.

This ensures each layer of material lines up properly — even a slight misalignment can derail the performance of the finished machine.

Making a motor

After perfecting the printing platform, the researchers fabricated a linear motor, which generates straight-line motion (as opposed to a rotating motor, like the one in a car). Linear motors are used in applications like pick-and-place robotics, optical systems, and baggage conveyers.

They fabricated the motor in about three hours and only needed to magnetize the hard magnetic materials after printing to enable full functionality. The researchers estimate total material costs would be about 50 cents per device. Their 3D-printed motor was able to generate several times more actuation than a common type of linear engine that relies on complex hydraulic amplifiers. 

“Even though we are excited by this engine and its performance, we are equally inspired because this is just an example of so many other things to come that could dramatically change how electronics are manufactured,” says Velásquez-García.

In the future, the researchers want to integrate the magnetization step into the multimaterial extrusion process, demonstrate the fabrication of fully 3D-printed rotary electrical motors, and add more tools to the platform to enable monolithic fabrication of more complex electronic devices.

This research is funded, in part, by Empiriko Corporation and the La Caixa Foundation.

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New study unveils the mechanism behind “boomerang” earthquakes

An earthquake typically sets off ruptures that ripple out from its underground origins. But on rare occasions, seismologists have observed quakes that reverse course, further shaking up areas that they passed through only seconds before. These “boomerang” earthquakes often occur in regions with complex fault systems. But a new study by MIT researchers predicts that such ricochet ruptures can occur even along simple faults.

The study, which appears today in the journal AGU Advances, reports that boomerang earthquakes can happen along a simple fault under several conditions: if the quake propagates out in just one direction, over a large enough distance, and if friction along the rupturing fault builds and subsides rapidly during the quake. Under these conditions, even a simple straight fault, like some segments of the San Andreas fault in California, could experience a boomerang quake.

These newly identified conditions are relatively common, suggesting that many earthquakes that have occurred along simple faults may have experienced a boomerang effect, or what scientists term “back-propagating fronts.”

“Our work suggests that these boomerang quakes may have been undetected in a number of cases,” says study author Yudong Sun, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “We do think this behavior may be more common than we have seen so far in the seismic data.”

The new results could help scientists better assess future hazards in simple fault zones where boomerang quakes could potentially strike twice.

“In most cases, it would be impossible for a person to tell that an earthquake has propagated back just from the ground shaking, because ground motion is complex and affected by many factors,” says co-author Camilla Cattania, the Cecil and Ida Green Career Development Professor of Geophysics at MIT. “However, we know that shaking is amplified in the direction of rupture, and buildings would shake more in response. So there is a real effect in terms of the damage that results. That’s why understanding where these boomerang events could occur matters.”

Keep it simple

There have been a handful of instances where scientists have recorded seismic data suggesting that a quake reversed direction. In 2016, an earthquake in the middle of the Atlantic Ocean rippled eastward, and then seconds later richocheted back west. Similar return rumblers may have occurred in 2011 during the magnitude 9 earthquake in Tohoku, Japan, and in 2023 during the destructive magnitude 7.8 quake in Turkey and Syria, among others.

These events took place in various fault regions, from complex zones of multiple intersecting fault lines to regions with just a single, straight fault. While seismologists have assumed that such complex quakes would be more likely to occur in multifault systems, the rare examples along simple faults got Sun and Cattania wondering: Could an earthquake reverse course along a simple fault? And if so, what could cause such a bounce-back in a seemingly simple system?

“When you see this boomerang-like behavior, it is tempting to explain this in terms of some complexity in the Earth,” Cattania says. “For instance, there may be many faults that interact, with earthquakes jumping between fault segments, or fault surfaces with prominent kinks and bends. In many cases, this could explain back-propagating behavior. But what we found was, you could have a very simple fault and still get this complex behavior.”

Faulty friction

In their new study, the team looked to simulate an earthquake along a simple fault system. In geology, a fault is a crack or fracture that runs through the Earth’s crust. An earthquake begins when the stress between rocks on either side of the fault, suddenly decreases, and one side slides against the other, setting off seismic waves that rupture rocks all along the fault. This seismic activity, which initiates deep in the crust, can sometimes reach and shake up the surface.

Cattania and Sun used a computer model to represent the fundamental physics at play during an earthquake along a simple fault. In their model, they simulated the Earth’s crust as a simple elastic material, in which they embedded a single straight fault. They then simulated how the fault would exhibit an earthquake under different scenarios. For instance, the team varied the length of the fault and the location of the quake’s initation point below the surface, as well as whether the quake traveled in one versus two directions.

Over multiple simulations, they observed that only the unilateral quakes — those that traveled in one direction — exhibited a boomerang effect. Specifically, these quakes seemed to include a type that seismologists term “back-propagating” events, in which the rumbler splits at some point along the fault, partly continuing in the same direction and partly reversing back the way it came.

“When you look at a simulation, sometimes you don’t fully understand what causes a given behavior,” Cattania says. “So we developed mathematical models to understand it. And we went back and forth, to ultimately develop a simple theory that tells you should only see this back-propagation under these certain conditions.”

Those conditions, as the team’s new theory lays out, have to do with the friction along the fault. In standard earthquake physics, it’s generally understood that an earthquake is triggered when the stress built up between rocks on either side of a fault, is suddenly released. Rocks slide against each other in response, decreasing a fault’s friction. The reduction in fault friction creates a positive feedback that facilitates further sliding, sustaining the earthquake.

However, in their simulations, the team observed that when a quake travels along a fault in one direction, it can back-propagate when friction along the fault goes down, then up, and then down again.

“When the quake propagates in one direction, it produces a “breaking’’ effect that reduces the sliding velocity, increases friction, and allows only a narrow section of the fault to slide at a time,” Cattania says. “The region behind the quake, which stops sliding, can then rupture again, because it has accumulated more stress to slide again.”

The team found that, in addition to traveling in one direction and along a fault with changing friction, a boomerang is likely to occur if a quake has traveled over a large enough distance.

“This implies that large earthquakes are not simply ‘scaled-up’ versions of small earthquakes, but instead they have their own unique rupture behavior,” Sun says.

The team suspects that back-propagating quakes may be more common than scientists have thought, and they may occur along simple, straight faults, which are typically older than more complex fault systems.

“You shouldn’t only expect this complex behavior on a young, complex fault system. You can also see it on mature, simple faults,” Cattania says. “The key open question now is how often rupture reversals, or ‘boomerang’ earthquakes, occur in nature. Many observational studies so far have used methods that can’t detect back-propagating fronts. Our work motivates actively looking for them, to further advance our understanding of earthquake physics and ultimately mitigate seismic risk.”

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MIT community members elected to the National Academy of Engineering for 2026

Seven MIT researchers are among the 130 new members and 28 international members recently elected to the National Academy of Engineering (NAE) for 2026. Twelve additional MIT alumni were also elected as new members.

One of the highest professional distinctions for engineers, membership in the NAE is given to individuals who have made outstanding contributions to “engineering research, practice, or education,” and to “the pioneering of new and developing fields of technology, making major advancements in traditional fields of engineering, or developing/implementing innovative approaches to engineering education.”

The seven MIT electees this year include:

Moungi Gabriel Bawendi, the Lester Wolfe Professor of Chemistry in the Department of Chemistry, was honored for the synthesis and characterization of semiconductor quantum dots and their applications in displays, photovoltaics, and biology.

Charles Harvey, a professor in the Department of Civil and Environmental Engineering, was honored for contributions to hydrogeology regarding groundwater arsenic contamination, transport, and consequences.

Piotr Indyk, the Thomas D. and Virginia W. Cabot Professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory, was honored for contributions to approximate nearest neighbor search, streaming, and sketching algorithms for massive data processing.

John Henry Lienhard, the Abdul Latif Jameel Professor of Water and Mechanical Engineering in the Department of Mechanical Engineering, was honored for advances and technological innovations in desalination.

Ram Sasisekharan, the Alfred H. Caspary Professor of Biological Physics and Physics in the Department of Biological Engineering, was honored for discovering the U.S. heparin contaminant in 2008 and creating clinical antibodies for Zika, dengue, SARS-CoV-2, and other diseases.

Frances Ross, the TDK Professor in the Department of Materials Science and Engineering, was honored for ultra-high vacuum and liquid-cell transmission electron microscopies and their worldwide adoptions for materials research and semiconductor technology development.

Zoltán Sandor Spakovszky SM ’99, PhD ’01, the T. Wilson (1953) Professor in Aeronautics in the Department of Aeronautics and Astronautics, was honored for contributions, through rigorous discoveries and advancements, in aeroengine aerodynamic and aerostructural stability and acoustics.

“Each of the MIT faculty and alumni elected to the National Academy of Engineering has made extraordinary contributions to their fields through research, education, and innovation,” says Paula T. Hammond, dean of the School of Engineering and Institute Professor in the Department of Chemical Engineering. "They represent the breadth of excellence we have here at MIT. This honor reflects the impact of their work, and I’m proud to celebrate their achievement and offer my warmest congratulations.”

Twelve additional alumni were elected to the National Academy of Engineering this year. They are: Anne Hammons Aunins PhD ’91; Lars James Blackmore PhD ’07; John-Paul Clarke ’91, SM ’92, SCD ’97; Michael Fardis SM ’77, SM ’78, PhD ’79; David Hays PhD ’98; Stephen Thomas Kent ’76, EE ’78, ENG ’78, PhD ’81; Randal D. Koster SM ’85, SCD ’88; Fred Mannering PhD ’83; Peyman Milanfar SM ’91, EE ’93, ENG ’93, PhD ’93; Amnon Shashua PhD ’93; Michael Paul Thien SCD ’88; and Terry A. Winograd PhD ’70.

de MIT News https://ift.tt/rsft9K3