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KAIST Study Finds Politically Salient Immigration Issues Can Lead to Higher Industrial Pollution
When immigration or refugee issues become heated political topics, nearby factories may end up releasing more toxic substances. Although the two phenomena may appear unrelated, a KAIST-led international research team has found that they are in fact connected through the government’s limited administrative and fiscal resources.
KAIST (President Choongsik Bae) announced on the 10th of July that a joint research team led by Professor Narae Lee from The School of Business and Technology Management at KAIST, in collaboration with Professor Heli Wang from Singapore Management University (SMU), analyzed immigration-related legislation and environmental data across the United States and found that when immigration becomes a central political agenda, government environmental oversight weakens and firms’ toxic chemical releases increase. The research team describes this phenomenon as “institutional crowding.”
Government administrative capacity and budgets are not unlimited. When a new political issue emerges, government attention and resources become concentrated in that area. In the process, enforcement in relatively less visible policy areas, such as environmental oversight, may weaken. Although the research team analyzed immigration as a case study, they explain that this phenomenon is not limited to a specific issue. Rather, it represents a general mechanism that can arise when political agendas compete for limited government resources.
The research team combined data from the U.S. Environmental Protection Agency’s Toxics Release Inventory (TRI) with immigration-related legislative data from U.S. states. By analyzing a total of 82,377 observations collected from 14,390 manufacturing facilities across the United States between 2010 and 2018, the team found that each additional immigration-related bill was associated with an average increase of about 1% in toxic chemical releases per manufacturing facility. This is equivalent to approximately 25 kilograms, or 56 pounds, of additional toxic emissions per facility.
The researchers found that this increase was not caused by a relaxation of environmental regulatory standards. Rather, it occurred because firms reduced costly efforts to cut pollution and treat toxic waste as government environmental oversight became relatively less effective.
This pattern was especially pronounced in states facing fiscal constraints. In states with high debt or heavy fiscal burdens, environmental oversight weakened further when political attention shifted to new issues.This suggests that when government budgets are tight, resources are more likely to be allocated first to politically urgent issues, while environmental monitoring may be pushed down the priority list.
Professor Narae Lee said, “This study does not argue that immigration causes environmental pollution. Rather, it shows that shifts in the political agenda item can weaken environmental oversight and thereby increase corporate pollution,” adding, “Even when limited government resources are concentrated on a particular issue, environmental oversight needs to be institutionally protected so that it remains stable.”
The study is significant in that it empirically identifies how competition among political agendas can affect firms’ environmental pollution management. It also offers new implications for public policy and for advancing environmental justice, so that the burden of environmental pollution does not fall disproportionately on socially vulnerable groups.
The research was published online on May 29 in the Journal of Management, a leading international journal in the field of management, with Professor Narae Lee as the first author.
An earlier version of the paper received the POSCO Corporate Citizenship Research Award, the Robert J. Litschert Award from the Academy of Management, and the Best Paper with Practical Implications Award from the Strategic Management Society, recognizing the excellence and practical significance of the research.
※ Paper title: There’s More Than Meets the Eye: Assessing the Impact of Immigrants on Firm Environmental Performance, DOI: https://doi.org/10.1177/01492063261442451
2026.07.10 View 90 -
KAIST Automates the Search for “Dream Semiconductor” 2D Semiconductors
The era of researchers manually searching for two-dimensional semiconductors, which are drawing attention as next-generation AI semiconductors, is coming to an end. KAIST researchers have automated semiconductor screening and device fabrication, analyzed thousands of devices, and revealed the relationship between thickness and performance that had long been difficult to identify. This achievement is expected to shift next-generation semiconductor research toward a data-driven approach and accelerate the commercialization of AI semiconductors and ultra-low-power semiconductors.
KAIST (President Choongsik Bae) announced on the 9th that a research team led by Professor Jimin Kwon of the School of Electrical Engineering and the Department of AI System has developed a technology that automatically identifies two-dimensional semiconductors from optical microscope images alone and connects the process to transistor fabrication, through joint research with UNIST, Hanbat National University, Hanyang University, and Washington University in St. Louis in the United States.
Two-dimensional semiconductors are ultrathin semiconductors only a few atomic layers thick. They are called “dream semiconductors” because they can enable smaller semiconductors that consume less electricity than conventional silicon semiconductors. Today’s silicon semiconductors are approaching physical limits, as continued miniaturization of circuits leads to greater power loss and heat generation. Two-dimensional semiconductors, which are attracting attention as next-generation materials to overcome these limits, are expected to be used in a wide range of future technologies, including AI semiconductors, smartphones, data centers, wearable devices, foldable or stretchable electronics, and ultra-small medical sensors.
However, in two-dimensional semiconductors made through solution processing, the position, size, and thickness of each small semiconductor flake all differ, requiring researchers to find the desired samples one by one under a microscope. They then had to manually design electrodes according to the identified positions, requiring substantial time and effort, and making it practically difficult to analyze thousands or more devices at once.
The research team used molybdenum disulfide (MoS₂), a representative two-dimensional semiconductor material. By using the fact that the RGB red, green, and blue brightness values seen under a microscope change depending on thickness, the team enabled a computer to automatically identify the desired semiconductor and automatically design the electrodes. Verification using atomic force microscopy (AFM) confirmed that even subtle thickness differences of three to eight layers could be accurately distinguished.
Through this approach, the team successfully selected suitable samples automatically from more than 120,000 semiconductor flakes and fabricated and analyzed 1,615 transistors.
The large-scale analysis also produced meaningful results. The team statistically clarified for the first time that as the semiconductor becomes thicker, current flows more easily, but the ability to switch electricity on and off actually decreases. This characteristic had been difficult to confirm previously because only a small number of samples could be analyzed, but the team revealed it through large-scale data.
The greatest significance of this study is that it did not simply automate the fabrication process, but transformed two-dimensional semiconductor research, which had relied on human experience, into data-driven research. Going forward, the technology is expected to enable researchers to fabricate and analyze more semiconductors more quickly, identify high-performance materials, and ultimately expand into research in which AI designs new semiconductors.
This study was conducted with Professor Jimin Kwon, Dr. Haksoon Jung, and Dr. Yongwoo Lee of KAIST as co-corresponding authors, and Sanghyun Lee of UNIST as the first author. The research results were published on April 3 in Advanced Functional Materials, a leading international journal in materials science, and were also selected as an Inside Back Cover article in the field of 2D Materials & Electronics.
※ Paper title: Statistically Resolving Thickness-Dependent Electrical Characteristics in Multilayer-MoS₂ Transistors, DOI: 10.1002/adfm.202532204
※ Author information: Professor Jimin Kwon (KAIST, corresponding author), Dr. Haksoon Jung (KAIST, corresponding author), Dr. Yongwoo Lee (KAIST, corresponding author), Sanghyun Lee (UNIST, first author), and participating researchers from partner institutions: Sumin Hong (UNIST), Minho Park (UNIST), Professor Seongju Kim (Hanbat National University), Professor Sang-Hoon Baek (Hanyang University), Professor Joonki Suh (KAIST), Seonguk Yang (KAIST), Professor Sang-Hoon Bae (Washington University in St. Louis), and Dr. Chang-Soo Lee (TDS)
This research was supported by the Individual Basic Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT), and by the Advanced Strategic Industry Super-Gap Technology Development Program of the Korea Planning & Evaluation Institute of Industrial Technology (KEIT), funded by the Ministry of Trade, Industry and Energy (MOTIE).
2026.07.09 View 211 -
KAIST Develops Core Display Technology That Prevents Image Distortion Even When Stretched
Beyond bendable and foldable displays, the era of stretchable displays, whose screens can expand freely like rubber, is now emerging. KAIST researchers have developed a core technology that allows text, images, and other on-screen information to retain their original shape even when the screen is stretched by up to 15%. The achievement is expected to help solve the problem of image distortion and accelerate the commercialization of next-generation high-quality stretchable displays.
KAIST (President Choongsik Bae) announced on the July 8 that a research team led by Professor Seunghyup Yoo of the School of Electrical Engineering, in collaboration with Professor Hanul Moon’s team at Dong-A University (President Hae Woo Lee), has successfully implemented an auxetic-based stretchable display platform. Auxetic structures expand in both width and length when pulled, allowing the display to stretch uniformly at the same ratio in all directions without distorting the image on the screen.
Conventional stretchable displays are generally made by forming light-emitting devices on a stretchable substrate, which serves as the base layer of the display. However, when such a substrate is stretched in one direction, it tends to shrink in the opposite direction, causing letters and images on the screen to become flattened or distorted. Auxetic structures have been used to address this problem, but most previous approaches were limited to maintaining the overall horizontal-to-vertical ratio of the screen, while the letters and images within the screen still remained vulnerable to distortion.
Instead of bonding the auxetic structure and the stretchable substrate across the entire surface, as in conventional methods, the research team proposed a new design approach that uses computational analysis to selectively connect only the necessary points that ensure isotropic expansion throughout the substrate.
In the conventional approach, the twisting deformation that occurs as the auxetic structure stretches is directly transferred to the substrate, distorting the image inside the screen. In contrast, the platform developed by the research team was designed so that each region moves evenly outward from its original position. This allows not only the entire screen but also small areas such as letters and images to expand together while maintaining their original shapes.
The research team verified the platform’s performance by repeatedly stretching a substrate patterned with letters and images in both the horizontal and vertical directions. In the conventional method, the patterns underwent local deformation, whereas in the new platform, the shapes of the letters and images remained intact. This demonstrates that not only the whole screen but also fine images on-screen can expand uniformly without distortion.
The team also integrated an LED array, a structure in which multiple LEDs are arranged at regular intervals, onto the platform to verify its performance as an working stretchable display. Even when stretched by up to 15% in both the horizontal and vertical directions, stable electrical operation and the screen brightness were maintained. After repeated stretching to 15%, the decrease in brightness remained below 2%, confirming the platform’s potential for practical display applications.
This technology is expected to serve as a core platform for next-generation electronics with freely changeable shapes, including wearable electronic devices, electronic skin, or e-skin, which refers to electronic devices that stretch like skin while sensing and displaying information, medical biosensors, soft robots, and curved displays for automobiles and aircraft.
Professor Seunghyup Yoo of KAIST said, “For stretchable displays to be used as actual information display devices, they must not only stretch well, but also preserve on-screen information accurately during stretching,” adding, “This platform enables uniform expansion from small areas of the screen to the entire display, and will serve as a key foundational technology for accelerating the commercialization of high-quality stretchable displays.”
This study was led by KAIST Dr. Su-Bon Kim and Dr. Junho Kim as co-first authors, with Professor Hanul Moon of Dong-A University and Professor Seunghyup Yoo of KAIST as co-corresponding authors. The research was published in the international journal Nature Communications on June 10.
※ Paper title: Hybrid auxetic metamaterial platforms enabling multiscale isotropic expansion for distortion-free stretchable displays, DOI: 10.1038/s41467-026-74141-6
This research was supported by the National Research Foundation of Korea (NRF) Mid-Career Researcher Program, the Future Display Strategic Research Laboratory Program, the Korea Planning & Evaluation Institute of Industrial Technology (KEIT), and the Korea Institute for Advancement of Technology (KIAT) HRD Program.
2026.07.08 View 251 -
KAIST Enables DNA Synthesis Using Only Temperature Instead of Chemical Reagents
"Complex chemical processes are essential for making DNA." This long-held assumption in the field of biotechnology has been overturned by a Korean research team. A KAIST research team has developed the world's first foundational technology that enables the synthesis of desired DNA using only temperature. Using this technology, the team also demonstrated a "DNA temperature black box" that records temperature changes during shipping without electricity.
KAIST announced on the 7th of July that a research team led by Professor Yeongjae Choi of the Graduate School of Engineering Biology, in collaboration with ATG Lifetech Inc. (CEO Taehoon Ryu) and a research team led by Professor Hansol Choi from the Department of Life Science at Ewha Womans University, has developed this platform technology that synthesizes desired DNA sequences by controlling only temperature.
DNA is the "blueprint" that contains the genetic information of humans and all other living organisms. Scientists use custom-made DNA in various biotechnology applications, such as diagnosing diseases, developing new drugs, and creating microorganisms with new functions. Until now, however, each time one of the four bases that make up DNA—A, T, G, and C—was connected, chemical reagents had to be added and washed out repeatedly. As a result, costly automated DNA synthesis equipment and specialized research facilities were essential.
To overcome these limitations, the research team developed "hairpin DNA that reacts only at specific temperatures." This hairpin DNA is a special DNA structure that remains folded like a hairpin and unfolds only at a certain temperature. The team placed multiple types of hairpin DNA that operate at different temperatures into a single test tube and succeeded in synthesizing desired DNA step by step by changing only the temperature in the sequence.
This opens the way for synthesizing DNA with only a general temperature control device, without the need for complex reagent replacement or large-scale equipment.
As the technology advances, it is expected to greatly reduce the cost and time required to make DNA, lowering the entry barriers not only for synthetic biology and genetic research, but also for various bioindustries such as drug development and precision medicine.
To demonstrate the practical applicability of the technology, the research team also implemented a power-free "DNA temperature black box." This device is normally stored in a freeze-dried state and begins operating when a single drop of water is added just before use. It then automatically records—directly into a DNA sequence—when, how long, and in what order the temperature changes during shipping. In addition, when exposed to temperatures above a certain level, the device changes color, allowing abnormalities to be checked visually on the spot. It is expected to be used for the quality control of products for which cold-chain distribution is important, such as vaccines, biopharmaceuticals, cell therapies, and fresh foods.
KAIST researcher Jangho Choi and GIST doctoral student Jinho Kim participated in this research as co-first authors, and the research results were published in the international journal Nature Communications on July 2.
※ Paper title: Programmable one-pot polymerase-mediated DNA synthesis via temperature control
※ DOI: https://doi.org/10.1038/s41467-026-74890-4
※ Related Video: https://drive.google.com/file/d/1bUtzC83qIm1k-hNFKTb09yFPhfsD4iU-/view?usp=drive_lin
※ Authors: Jangho Choi (KAIST, co-first author), Jinho Kim (GIST, co-first author), Hansol Choi (Ewha Womans University, corresponding author), Yeongjae Choi (KAIST, corresponding author)
This research was supported by the Ministry of Science and ICT through the Future Promising Convergence Technology Pioneer Program, the Biofoundry-Based Technology Development Program, the Young Researcher Program, and the Global Basic Research Laboratory Program.
2026.07.07 View 221 -
KAIST Identifies the “Hidden Energy Cost” of AI Agents for the First Time
As the era of AI agents—systems that can reason and act autonomously—begins, the power consumption of data centers is emerging as a critical challenge. A KAIST research team has, for the first time, analyzed the computational cost and energy consumption of AI agents, finding that they can consume up to 136.5 times energy per query than conventional generative AI. The study shows that competitiveness in the AI era is expanding beyond model performance to include the efficiency of data centers and power infrastructure.
KAIST announced that a research team led by Professor Minsoo Rhu of the School of Electrical Engineering has systematically analyzed, for the first time, how much computational resources and power AI agents require in real-world service environments.
Large language model (LLMs) powered applications such as ChatGPT have rapidly evolved beyond simply answering questions. They are now developing into AI agents: next-generation AI systems that can plan, use external tools such as web search, calculators, and code execution environments, and solve complex tasks by coordinating multiple steps on their own.
Although AI agents are increasingly being adopted in areas such as software development, research, and workplace automation, little has been known about the amount of electricity and operational cost required to run them in practice.
The research team defined AI agents not merely as software programs, but as a new type of workload that must be continuously processed by data-center servers and graphics processing units, or GPUs—high-performance chips used for large-scale AI computation. The team then analyzed the computational load and energy consumption incurred during actual AI agent execution.
The analysis found that AI agents perform, far higher volumes of LLM invocations than conventional chain-of-thought reasoning. Chain-of-thought, or CoT, refers to a method in which an AI model breaks down its reasoning process step by step to reach an answer, while an LLM invocation refers to each computational request made to a language model to generate a new judgment or response.
Because AI agents repeatedly call language models during execution, their response latency also increases significantly. The team found that response time can increase by up to 153.7 times, while GPUs remain idle for as much as 54.5 percent of the total execution time as external tools perform their tasks. In other words, as AI systems take on more complex tasks, a new form of inefficiency emerges in which expensive GPUs cannot be fully utilized.
The research team also analyzed the power consumption of AI agents at data-center scale. An AI agent using a 70-billion-parameter LLM—a scale comparable to current commercial AI services—consumed an average of 348.41 watt-hours per query. This is 136.5 times higher than the energy consumed by a conventional generative AI system performing simple question answering.
In addition, the team projected a future scenario in which 13.7 billion AI agent requests are generated per day — a volume equivalent to current Google search traffic. Under this scenario, data-center power demand would reach approximately 198.9 gigawatts, a level far exceeding the scale of AI data centers currently under development (which are in the range of a few gigawatts) and equivalent to roughly half of the average power consumption of the United States.
This study demonstrates that the focus of competition in the AI era is shifting from “smarter AI” to “optimally efficient AI.” Going forward, it will be essential not only to advance AI models, but also to jointly optimize AI semiconductors, data centers, and power infrastructure through co-design. Such an approach is expected to become a key strategy for reducing the operating cost of AI services and building sustainable AI infrastructure.
“This study is the first to quantitatively show not only how AI is becoming more intelligent, but also how much electricity and cost are required to implement and sustain that intelligence,” said Professor Rhu. “As AI agents become widespread, it will become increasingly important to take an integrated co-design approach that optimizes not only AI data-center infrastructure, but also AI agent models and power infrastructure.” He added, “Research and investment in this direction will be essential to dramatically reduce the cost for end users to access AI services while building sustainable AI infrastructure.”
The study was conducted with Jiin Kim, a Ph.D. student in the KAIST School of Electrical Engineering, as the first author. The paper was presented in February at the 32nd IEEE International Symposium on High-Performance Computer Architecture, or HPCA, one of the most prestigious international conferences in computer system design. The research team has also released the AI agent implementations and benchmarks used in the paper as open source to support follow-up studies by researchers worldwide.
Paper title: “The Cost of Dynamic Reasoning: Demystifying AI Agents and Test-Time Scaling from an AI Infrastructure Perspective”
Open-source repository: 10.1109/HPCA68181.2026.11408569
This research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) through the SW Starlab program, the K-Cloud Technology Development Program using AI semiconductors, and the Leading Technology Development Program for Advancing AI-Semiconductor-Based Data Centers, as well as by the Samsung Electronics Future Technology Incubation Center.
2026.07.07 View 298 -
KAIST Global Entrepreneurship Summer School Marks Fifth Consecutive Year of Cultivating Future Entrepreneurs in Silicon Valley
The 2026 Global Entrepreneurship Summer School (GESS), organized by the KAIST Office of Global Initiatives, has successfully concluded its fifth annual program.
Now in its fifth year, GESS has become KAIST's flagship global entrepreneurship program, providing students with firsthand experience in Silicon Valley—the world's leading startup ecosystem—and equipping them with the entrepreneurial mindset and global competencies needed to launch ventures on the international stage.
Participants in the 2026 GESS program, including both undergraduate and graduate students, were selected through a competitive process consisting of document screening, interviews, team presentations, and peer evaluations.
Prior to traveling to Silicon Valley, the selected students completed a four-month preparatory program that included team building, customer discovery, business model development, and pitch preparation. Throughout the program, they received mentoring from entrepreneurs, venture investors, and industry experts, enabling them to refine their business ideas and evaluate their potential for entering global markets.
The Silicon Valley program, held in late June, was organized in collaboration with leading startup support organizations, including KOTRA Silicon Valley, IBK Changgong Silicon Valley, and Plug and Play. Through meetings with entrepreneurs, venture capitalists, and representatives from global technology companies, students gained firsthand insight into the Silicon Valley startup ecosystem while developing a deeper understanding of global markets.
For the fourth consecutive year, students from the KAIST College of Business Impact MBA program also participated in the Silicon Valley program, creating valuable opportunities for interdisciplinary collaboration and exchange among students with diverse academic backgrounds and professional experiences.
A highlight of this year's program was a startup storytelling workshop conducted in collaboration with educators from Stanford University. The workshop helped students strengthen their communication skills by learning how to present their ideas more persuasively—an essential competency for aspiring global entrepreneurs.
In partnership with KAIST alumni based in Silicon Valley, participants also visited leading global technology companies and unicorn startups, including Meta, NVIDIA, and Moloco. They attended networking events with local professionals and alumni, gaining firsthand exposure to the innovation culture and growth strategies of global technology companies while broadening their perspectives on international careers and entrepreneurship.
To put into practice one of the core values of entrepreneurship—creating positive social impact—GESS participants also organized "Let's Play AI+Tech," a community outreach program for elementary school students from underserved families in Sunnyvale, California. Designed and led entirely by KAIST students, the program introduced fundamental concepts in artificial intelligence through engaging, hands-on activities for children and their parents. The initiative also offered KAIST students a meaningful opportunity to give back to the local community while sharing their expertise in AI and technology.
The program concluded with the Final Pitch Competition, where each team presented the business models they had developed over several months to a panel of Silicon Valley investors and entrepreneurship experts. Through expert feedback and evaluation, participants had the opportunity to validate the global potential of their ventures.
Following a highly competitive final round, Team CUPID was named the overall winner. Team CUPID presented an AI-powered developer platform that automatically routes coding tasks to the most cost-effective AI model, significantly reducing developers' AI usage costs. The team received high praise from the judges for its clear problem definition, strong market potential, and scalability in the global market.
Gianidita Nurani Pertiwi, a member of Team CUPID and a student in the Department of Bio and Brain Engineering, said, "GESS provided an invaluable opportunity to experience Silicon Valley's entrepreneurial ecosystem firsthand. Through conversations with founders, investors, and industry experts, I learned how to refine our ideas from a global perspective. The experience has motivated me to continue pursuing innovation that can create meaningful impact beyond borders."
The 2026 GESS program has been organized for the fifth consecutive year by the Office of Global Initiative in collaboration with the Impact MBA program and the Startup KAIST. KAIST will continue strengthening partnerships with Silicon Valley and other global innovation hubs to nurture entrepreneurial talent capable of leading future industries worldwide.
2026.07.06 View 257 -
KAIST: Dementia-Causing Substance Turns On a Therapeutic “Switch”
A substance that worsens dementia has become a “switch” that initiates treatment. KAIST researchers have developed a new therapeutic approach that uses hydrogen peroxide (H₂O₂), a reactive oxygen species that damages cells and increases in the brains of patients with Alzheimer’s disease, to activate a drug selectively in diseased brain tissue. The team also confirmed improvements in cognitive function through animal experiments, presenting a new possibility for next-generation dementia treatment.
KAIST announced on the 2nd that a research team led by Professor Mi Hee Lim of the Department of Chemistry, in collaboration with Professor Mingeun Kim of Chonnam National University, Dr. Chul-Ho Lee and Dr. Kyoung-Shim Kim of the Korea Research Institute of Bioscience and Biotechnology, and Dr. Young-Ho Lee of the Korea Basic Science Institute, has developed a prodrug that is activated selectively in the diseased brain in Alzheimer’s disease and confirmed its therapeutic effects through animal experiments.
A prodrug is a drug that initially has minimal therapeutic effect but is converted into an active therapeutic agent only under specific conditions inside the body. In this study, the prodrug was designed to be activated only when it encounters hydrogen peroxide, which increases in the brains of patients with Alzheimer’s disease, allowing it to function as a “smart therapeutic agent” that selectively acts in diseased brain tissue.
In the brains of Alzheimer’s disease patients, hydrogen peroxide, which damages cells, is elevated above normal levels. Until now, it has generally been regarded only as a harmful substance that should be removed. However, the research team devised a method to use it instead as a signal that activates a drug.
The prodrugs developed by the research team, BE-1 and BE-2, are designed to remain minimally reactive in a healthy brain. However, when they encounter hydrogen peroxide in a brain affected by dementia, they are converted into active therapeutic compounds, AP-1 and AP-2. Through this process, they reduce reactive oxygen species, including hydrogen peroxide, while also preventing amyloid beta (Aβ) peptides — peptides known as a major cause of dementia that accumulate in the brain and damage nerve cells — from aggregating into highly toxic clumps.
Using advanced analytical techniques, the research team confirmed that the activated drug alters the morphology of amyloid beta aggregates and suppresses their growth into large aggregates.
These effects were also confirmed in Alzheimer’s disease mouse models. The drug crossed the blood-brain barrier (BBB), a protective barrier that controls whether substances in the blood can enter the brain, and was converted into the therapeutic compound inside the diseased brain. In mice that received long-term drug administration, oxidative stress in the hippocampus, which is responsible for memory, was reduced, and amyloid beta accumulation in the brain also decreased. In behavioral experiments assessing the ability to recognize new objects and navigate mazes, cognitive function was also found to improve.
This study is significant in that the drug was designed to operate only where needed by using the environment of the diseased brain itself. This approach presents a new strategy for dementia treatment that can enhance therapeutic efficacy while reducing side effects, and it is expected to be applicable to the treatment of other neurodegenerative diseases, such as Parkinson’s disease.
Professor Mi Hee Lim of KAIST’s Department of Chemistry said, “This study is meaningful in that hydrogen peroxide, which had previously been regarded only as something to be eliminated, was used as a signal to activate a drug. We expect this strategy, which activates drugs in diseased tissue, to become a new platform for treating complex diseases such as Alzheimer’s disease more safely and effectively.”
This study was co-first-authored by Jimin Lee and Eunseo Hong, Ph.D. candidates in KAIST’s Department of Chemistry, and was published online on May 31, 2026, in the international journal Small (Impact Factor: 12.1, top 10% in the field of chemistry).
※ Paper title: A Prodrug Approach for Activity-Based Chemical Modulation toward Multiple Pathological Targets in Alzheimer’s Disease
DOI: 10.1002/smll.74013
This research was supported by the National Research Foundation of Korea’s Leader Researcher Program, Global Leading Research Center Program, Sejong Science Fellowship, Graduate Student Research Encouragement Program, and institutional programs of KRIBB and KBSI.
2026.07.03 View 460 -
KAIST Develops AI That Reads Animal Behavior Like Language
An artificial intelligence model capable of reading and interpreting animal behavior like language has been developed by researchers at KAIST. The team created BehaVERT, an AI model that learns behavioral data in a manner similar to natural language and was able to independently identify social behavioral deficits in an autism mouse model, opening a new avenue for interpretable neuroscience.
KAIST (President Kwang-Hyung Lee) announced that a research team led by Professor Dae-Soo Kim from the Department of Brain and Cognitive Sciences has developed an AI model that interprets animal movements as a form of behavioral language.
The researchers transformed skeletal movements of mice into tokens, analogous to words in natural language, and trained a transformer-based model to learn behavioral meaning. The resulting model, named BehaVERT, successfully identified core social behavioral abnormalities in an autism mouse model without being provided any prior biological knowledge.
The study introduces a novel AI framework for analyzing animal behavior through language-based representations. Beyond simple behavior classification, the model demonstrates the ability to uncover biologically meaningful patterns and may serve as a foundation for next-generation behavioral foundation models applicable to drug discovery, psychiatric research, and behavioral genetics.
Inspired by the idea that animal behavior may possess structures similar to language, the researchers represented the positions of a mouse's nose, ears, spine, limbs, and tail as behavioral tokens and trained a BERT-based transformer architecture.
As a result, BehaVERT learned not only to classify behaviors but also to understand their contextual meaning over time, much like language models infer meaning from sequences of words.
The model achieved state-of-the-art performance across five international benchmark datasets covering social interaction, multi-animal behavior, three-dimensional motion analysis, and autism-related behavioral assessment.
Importantly, BehaVERT also provides interpretability, allowing researchers to visualize which behavioral cues influenced its decisions.
In experiments distinguishing Shank3B knockout autism-model mice from healthy controls, the AI consistently focused on oral-oral contact behavior. This finding aligns with previous biological studies showing that autism-model mice exhibit deficits in social interaction despite maintaining normal approach behavior.
In other words, the AI independently rediscovered a key biological characteristic solely from behavioral observations, without explicit biological instruction.
The researchers further found that the model's internal representation space organized behavioral features such as mobility, attention, and social engagement into structured patterns. This suggests that animal behavior, much like language, may possess an underlying semantic structure.
The study also highlights an unusual interdisciplinary achievement. The first author, Dr. Seungjae Shin, and other members of the research team were trained primarily in biology rather than artificial intelligence. By independently learning transformer architectures and deep learning techniques, they designed specialized models and training strategies tailored for behavioral analysis.
Professor Kim's laboratory has long pursued AI-driven behavioral analysis and previously developed AVATAR, a technology that reconstructs rodent behavior in virtual environments, leading to the founding of Actnova Inc.
"The project began with a simple question: Could animal movements contain a structure similar to language?" said Dr. Seungjae Shin, the first author of the study.
The team also adopted a self-supervised learning framework that enables AI to learn directly from behavioral data without manual annotations. Furthermore, a model trained on rat behavior successfully transferred to mouse behavior analysis, demonstrating the feasibility of a behavioral foundation model applicable across species.
"BehaVERT goes beyond behavior classification and enables the interpretation of behavioral meaning," said Professor Dae-Soo Kim. "We expect it to become a key research tool for discovering new insights in drug development, psychiatric disorders, behavioral genetics, and many other areas of life sciences."
The study was published on March 24, 2026, in the International Journal of Computer Vision (IJCV), one of the world's leading journals in computer vision.
Paper Information
• Title: BehaVERT: A Transformer-Based Motion Language Model for Decoding Behavioral Semantics in Mice
• Journal: International Journal of Computer Vision (IJCV)
• DOI: 10.1007/s11263-026-02834-y
Related Videos
• BehaVERT — Social Behavior Analysis Visualization (Investigation & Mount), https://youtu.be/JshCr-ZBQR0
• BehaVERT — Social Behavior Analysis Visualization (Investigation & Attack), https://youtu.be/p9RPhZM__Js
• BehaVERT — AI Discovers Core Social Behavioral Features in an Autism Mouse Model, https://youtu.be/D6zUyDu3t9I
Funding
This research was supported by the Mid-Career Researcher Program and the Brain Convergence Technology Development Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT), Republic of Korea.
2026.07.01 View 737 -
How Does Superconductivity Begin? Unveiling the Hidden Flow of Electrons
Superconductivity, a phenomenon where electricity flows without resistance, is considered the core of quantum computers and next-generation power technologies. However, the exact states electrons undergo before superconductivity emerges have not yet been fully elucidated. KAIST researchers have provided experimental clues revealing the hidden order electrons form prior to superconductivity in a kagome metal, a material closely related to superconducting phenomena. The team confirmed that a loop-like circulating order of electrons (loop-current order) emerges earlier than the periodic clustering of electrons (charge density wave).
KAIST (President Kwang Hyung Lee) announced on the 30th that a joint research team led by Professors Yeongkwan Kim, Myung Joon Han, and SungBin Lee from the Department of Physics discovered through circular dichroism angle-resolved photoemission spectroscopy (CD-ARPES) experiments and theoretical calculations that time-reversal symmetry breaking occurs at a higher temperature than the charge density wave formation in the kagome metal CsV3Sb5. Time-reversal symmetry is a property where physical phenomena appear identical even when time is reversed. The breaking of this symmetry implies that electrons within the material may have created a hidden flow with a specific directionality.
A kagome metal is a material with a repeating triangular atomic arrangement, resembling the traditional Japanese basket weaving pattern 'kagome'. In this structure, electrons interact strongly with each other, giving rise to various quantum phenomena rarely seen in normal metals, such as charge density waves, superconductivity, and topological electronic states. In particular, CsV3Sb5 exhibits both charge density waves and superconductivity at low temperatures, drawing attention as a crucial platform for next-generation quantum materials research.
However, there has been an ongoing debate over whether another hidden electronic order exists between the charge density wave and superconductivity in this material. Although several experiments have reported signals suggesting broken time-reversal symmetry, it was unclear whether this phenomenon was a consequence of the charge density wave formation or an independent electronic order that emerges prior to it.
To resolve this debate, the research team alternately irradiated high-quality CsV3Sb5 single crystals with left- and right-circularly polarized light and precisely measured the difference in the intensity of the emitted electrons. They then eliminated spurious signals potentially caused by the experimental setup's geometry, isolating only the intrinsic signals originating from the symmetry breaking of the material itself.
As a result, they confirmed that the signal of time-reversal symmetry breaking already appears around 140~145 K, which is significantly higher than the charge density wave formation temperature of about 94 K. This supports the interpretation that electrons form a loop-current order—a microscopic loop-like circulation—before creating the charge density wave pattern. The loop-current order is an electronic order where electrons behave as if flowing along small loops within the atomic lattice; it was theoretically proposed long ago but has been difficult to verify experimentally.
The team also tracked how the electronic structure changed as the temperature was lowered. At high temperatures, a normal metallic state appeared; at lower intermediate temperatures, the loop-current order formed first. As the temperature decreased further, a complex state evolved where the charge density wave intertwined with the loop-current order, eventually leading to the superconducting state. This research proposes a hierarchical structure of phase transitions in CsV3Sb5, progressing from 'loop-current order → charge density wave → superconductivity'.
This achievement provides a crucial clue for understanding the fundamental principles of superconductivity. It is not yet fully understood what kind of order electrons form before superconductivity occurs, or which electronic orders compete or cooperate with superconductivity. By demonstrating the existence of an electronic state with broken time-reversal symmetry prior to the superconducting state, this study offers an important lead in understanding unconventional superconductivity, which operates differently from standard mechanisms.
Furthermore, this research is expected to help understand hidden electronic orders in other superconducting materials beyond kagome metals. In particular, it could serve as a reference for explaining the peculiar electronic state (pseudogap) prior to superconductivity, which has long been discussed in cuprate high-temperature superconductors.
Professor Yeongkwan Kim stated, "This research is the result of directly tracking the time-reversal symmetry breaking of a kagome metal within its electronic structure, which had previously only been discussed through indirect signals. By showing the sequence in which electrons form order before reaching superconductivity, we have presented a new reference point for research on unconventional superconductivity and strongly correlated quantum materials.“
Professor Myung Joon Han added, "The key point is that the circular dichroism signal observed in the experiment aligns perfectly with the electrons' orbital motion pattern (orbital angular momentum pattern) expected from the loop-current order. This is a case where we uncovered the microscopic origin of the hidden electronic order by combining experiment and theory.“
KAIST Department of Physics researchers Jaehun Cha, Hyunggeun Lee, and Sangjun Sim participated as co-first authors in this study. The research findings were published online in the international physics journal Nature Physics on June 15, 2026.
Paper Title: Evidence of time-reversal symmetry breaking above the charge density wave order in a kagome metal
DOI: https://doi.org/10.1038/s41567-026-03331-2
This research was supported by the Mid-Career Researcher Program and the Accelerator Manpower Training Program (Ministry of Science and ICT, National Research Foundation of Korea), the Korea Research Institute of Standards and Science (KRISS), the Air Force Office of Scientific Research (AFOSR), and the US Department of Energy's Basic Energy Sciences (DOE BES).
2026.07.01 View 1115 -
KAIST Identifies New Therapeutic Target by Revealing How Cancer ‘Hijacks’ the Blueprint for Blood Vessel Development
Anti-angiogenic therapies targeting VEGF have been widely used in cancer treatment, yet their long-term efficacy remains limited. Tumor vascular endothelial cells (TECs) exhibit high adaptive plasticity, enabling them to resist treatment and sustain tumor growth, but the molecular mechanism underlying this plasticity has remained poorly understood.
KAIST, led by President Kwang Hyung Lee, announced that a joint research team led by Professor Inkyung Jung (Department of Biological Sciences), Professor Ji Min Lee (Graduate School of Medical Science and Engineering), and Professor Gou Young Koh (Institute for Basic Science) has now uncovered the answer. By integrating cross-cancer single-cell transcriptomic and epigenomic atlases across eight solid tumor types with multiomic profiles, including 3D chromatin contact maps, of human embryonic stem cell (hESC)-derived vascular endothelial cell differentiation, the team demonstrated that TECs reactivate a gene regulatory program normally confined to the late progenitor stage of vascular development. Much like reusing an old blueprint rather than drawing up a new one, tumors co-opt this pre-existing developmental program to fuel blood vessel growth.
The team’s integrative framework combined single-cell RNA-seq and ATAC-seq across multiple tumor types with H3K27ac ChIP-seq, Hi-C-based 3D chromatin mapping across a dense time series of hESC-to-EC differentiation. This approach resolved the EC-progenitor specific regulatory program that defines the shared pro-angiogenic program between late EC progenitors and TECs.
Within this framework, integrin receptor (ITGAV) emerged as a functional mediator specifically upregulated in both late EC progenitors and TECs. Cell-to-cell interaction analysis identified multiple key ligands from tumor micro enviroment (TME) that reactivate the progenitor-associated gene regulatory program. Pharmacologic inhibition attenuated endothelial migration, invasion, and tube formation in vitro, and significantly reduced tumor vascularization and growth in a colorectal cancer xenograft model in vivo.
Professor Inkyung Jung noted that this study reframes how we understand tumor angiogenesis: tumors do not invent new mechanisms, but exploit regulatory programs already embedded in normal vascular development. This insight offers a new conceptual basis for why anti-VEGF therapies face limitations, and points toward targeting the underlying regulatory architecture of endothelial plasticity as a complementary anti-angiogenic strategy.
The study was co-first authored by Dr. Andrew J. Lee, Dr. Sunwoo Min, Ph.D. student Su Chan Park; and Dr. Mei-Yu Qiu. Professors Inkyung Jung, Ji Min Lee, and Gou Young Koh served as corresponding authors. The findings were published on June 8 in Cancer Research [IF = 22.3].
※ Paper title: "A Co-opted Developmental Gene Regulatory Program in Endothelial Progenitors Promotes Tumor Angiogenic Phenotypes"
※ DOI: 10.1158/0008-5472.CAN-25-5094
※ Authors: Andrew J. Lee (KAIST, first author), Sunwoo Min (KAIST, co-first), Su Chan Park (KAIST, co-first), Mei-Yu Qiu (IBS, co-first), Gou Young Koh (IBS, co-corresponding), Ji Min Lee (KAIST, co-corresponding), Inkyung Jung (KAIST, corresponding)
This research was supported by the National Research Foundation of Korea and the Institute for Basic Science.
2026.07.01 View 379 -
KAIST Identifies Hidden Age Bias in Artificial Intelligence
Do responses generated by artificial intelligence systems such as ChatGPT reflect social prejudice? A KAIST research team has quantitatively analyzed and identified age-related stereotypes embedded in the responses of generative artificial intelligence. The study sheds light on the potential impact of hidden AI biases on social perceptions and suggests directions for the development of more inclusive AI.
KAIST, led by President Kwang Hyung Lee, announced on the 28th that a research team led by Professor Moon Choi of the Graduate School of Science and Technology Policy quantitatively analyzed subtle stereotypes about older adults embedded in sentences generated by OpenAI’s generative AI model ChatGPT-4o.
Generative AI is now widely used in everyday information search and decision-making processes, but concerns have also been raised that it may reproduce social biases contained in its training data. While previous studies have primarily focused on biases related to gender or race, this study, conducted by Ph.D. student Wan Hong as the first author, is significant in that it examined ageism from the perspective of artificial intelligence at a time when the issue is becoming increasingly important amid global population aging. Ageism refers to discrimination against, or negative perceptions of, certain groups based on age.
The research team collected 900 text samples generated by GPT-4o using neutral prompts that asked the model to describe the characteristics of age groups from 10 to 90 in 10-year intervals. The team then analyzed the responses using the Stereotype Content Model, a major theory in social psychology that explains perceptions of people or groups along two dimensions: warmth and competence.
The analysis found that older adults, defined as those aged 60 and above, received high scores for “warmth,” a trait associated with kindness, trustworthiness, and consideration. However, their scores for “competence,” which refers to ability, expertise, and efficiency, tended to be relatively lower than those of younger age groups.
The generated responses also tended to portray the human life course as divided into three groups: youth, covering those in their teens and 20s; middle age, covering those in their 30s to 50s; and older adulthood, covering those in their 60s and above. In particular, descriptions of people aged 70 and older repeatedly showed relatively uniform characteristics.
The research team also focused on “assertiveness,” which refers to the tendency to actively express one’s opinions and act with confidence and initiative. The analysis showed that the frequency of expressions related to assertiveness decreased as age increased. This suggests that ChatGPT-4o tends to portray older adults as wise and caring, while representing their agency and active capacities as relatively lower.
This study is significant because it quantitatively identified subtle biases embedded in generative AI by combining social science theory with computational analysis techniques. The findings show that generative AI tends to portray older adults as a “warm but less competent” group, a pattern similar to typical stereotypes of older adults repeatedly found in mass media.
This study is significant because it quantitatively identified subtle biases embedded in generative AI by combining social science theory with computational analysis techniques. The findings show that generative AI tends to portray older adults as a “warm but less competent” group, a pattern similar to typical stereotypes of older adults repeatedly found in mass media.
“Bias in AI is not merely a technological issue, but a social one,” said Professor Moon Choi. “To build inclusive artificial intelligence, people from diverse generations must participate in the development process.”
The study was conducted with Ph.D. student Wan Hong of the Graduate School of Science and Technology Policy as the first author. The findings were published in the February 2026 special issue of The Gerontologist, a leading international journal in the field of gerontology with an impact factor of 5.7.
※ Paper title: “An Exploratory Semantic Analysis of Age-Related Stereotypes in OpenAI’s GPT-4o Model”
※ DOI: https://doi.org/10.1093/geront/gnaf291
This research was supported by the National Research Foundation of Korea through the Mid-Career Research Program for Convergence between Science and Technology and the Humanities and Social Sciences.
※ Research team homepage: https://aging.kaist.ac.kr
2026.07.01 View 361 -
Crude Oil Separates Without Boiling: KAIST and Georgia Tech Develop Energy-Saving Membrane Technology
An international research team led by KAIST has developed a membrane technology that could significantly reduce the energy required for crude oil refining by replacing part of the century-old distillation process.
KAIST(President Kwang Hyung Lee) announced that a team led by Professor Dong-Yeun Koh of KAIST, in collaboration with Professor Ryan Lively's group at Georgia Tech, demonstrated a simple and inexpensive membrane capable of separating crude oil at room temperature without heating. The research was published in Nature, one of the world's leading scientific journals.
Crude oil underpins modern life by providing not only transportation fuels but also essential feedstocks for plastics, packaging materials, textiles, and countless consumer products. Because the cost of refining directly influences the price of these products, technologies that reduce refining energy consumption can generate substantial economic and environmental benefits.
Traditionally, refineries separate crude oil through distillation, a process that heats crude oil above 350 °C to vaporize it and then cools the vapor to recover different fractions. Globally, crude oil distillation consumes approximately 1,100 terawatt-hours (TWh) of energy each year—equivalent to the annual output of about 130 nuclear power plants, each at gigawatt scale, operating continuously. As a result, distillation remains one of the largest sources of energy consumption and greenhouse gas emissions in the refining industry.
At the same time, increasing cost pressures in global petrochemical markets have intensified the need for more energy-efficient separation technologies.
Membrane-based crude oil fractionations have attracted increasing attention as a potential alternative. However, conventional wisdom has held that molecularly precise separation requires an ultrathin selective layer coated onto the membrane surface. While effective, such coatings increase manufacturing costs and are prone to defects when scaled to large areas, limiting industrial deployment.
To overcome this challenge, the researchers took a radically different approach. Instead of relying on a specialized coating, they passed crude oil directly through a bare porous polyacrylonitrile (PAN) membrane—a chemically stable and inexpensive polymer commonly used as a support material in industrial membranes.
As crude oil permeated through the membrane, heavy hydrocarbons selectively deposited on the pore walls, gradually narrowing the pores and creating self-assembled separation channels smaller than 2 nanometers. Rather than relying on a specially engineered coating, the crude oil itself created the nanoscale pathways needed for precise molecular separation.
Through these self-formed channels, lighter fractions such as naphtha, gasoline, and kerosene permeated rapidly, while heavier components were effectively retained. In a surprising reversal, membrane fouling—normally regarded as a performance-degrading phenomenon—became the very mechanism that enabled highly selective separation.
The bare PAN membrane delivered crude oil permeation rates approximately 23 times higher than those of previously reported state-of-the-art crude oil membranes while maintaining stable performance for 28 consecutive days.
Professor Ryan Lively (Georgia Tech) commented “one of the key challenges facing membrane systems for crude oil separation was the low productivities of the membrane units – the PAN membranes with their surprising separation mechanism – dramatically increase the productivity of the membrane unit, to the point where industry should seriously consider adopting the technology.”
Importantly, the technology can be integrated into existing refinery infrastructure as a modular filtration unit, avoiding major equipment replacement and reducing barriers to industrial adoption.
Process simulations showed that using the membrane as a pretreatment step before conventional distillation could reduce energy consumption by 31.6%, carbon dioxide emissions by 37.6%, cooling water usage by 20.7%, and operating costs by 36%.
If adopted throughout Korea's refining and petrochemical sector, the technology could reduce greenhouse-gas emissions by approximately 10 million tonnes annually—equivalent to the emissions of roughly four million internal combustion vehicles.
Beyond crude oil refining, the membrane platform could be applied to a broad range of chemical separation processes, including the purification of pyrolysis oil derived from waste plastics, the recovery of solvents used in battery manufacturing, pharmaceutical purification, and biofuel production. The researchers believe the technology could serve as a versatile platform for next-generation molecular separations across multiple industries.
Professor Dong-Yeun Koh of KAIST said, “This study reveals a new scientific principle in which a membrane interacts with a complex mixture and spontaneously forms its own separation channels. Working with real crude oil supplied by HD Hyundai Oilbank allowed us to validate the technology under conditions relevant to industrial operation.”
Professor Jae W. Lee of KAIST, a co-corresponding author of the study, added, “By advancing large-area membrane modularization and long-term operational reliability, we hope to broaden the adoption of membrane-based processes throughout the refining and petrochemical industries.”
Dr. Jihoon Choi and Dr. Hyeokjun Seo of KAIST, the study’s co-first authors, said, “Our goal is to precisely control this spontaneous pore-constriction phenomenon and develop it into a membrane platform applicable to the entire refining process. We also aim to expand the technology to plastic recycling, biofuel purification, and other sustainable chemical processes that support carbon neutrality.”
The study was co-first-authored by Dr. Jihoon Choi and Dr. Hyeokjun Seo of KAIST and was published online in Nature on June 24, 2026.
Paper Title: Crude Oil Fractionation by Means of Mesoporous Polyacrylonitrile Membranes
DOI 10.1038/s41586-026-10677-3
https://www.nature.com/articles/s41586-026-10677-3
This research was supported by the Ministry of Science and ICT of Korea through the Basic Research Program for Outstanding Early-Career Researchers and the Engineering Research Center (ERC) Program.
2026.06.25 View 1146