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KAIST Alumnus and TeamSparta CEO Beomgyu Lee Donates KRW 100 Million to Support AI Research
KAIST (President Choongsik Bae) announced on the 16th of July that Beomgyu Lee, CEO of AI education company TeamSparta, donated 100 million KRW (approximately $66,000 as of July 2026) to the School of Computing to support a research program centered on the use of generative AI agents.
With the support of this donation, KAIST plans to run a two-year program enabling master’s and doctoral researchers from all departments to use generative AI agents. Participating researchers will receive access to the latest AI development agents, including Claude Code and Codex, along with related training and regular seminars.
The donation ceremony was held on the 15th of July at KAIST's main campus in Daejeon and was attended by Beomgyu Lee; Jae-Gil Lee, Dean of the KAIST School of Computing; and Professor Sukyoung Ryu from the School of Computing. This marks Lee’s second private contribution, following his first donation in September 2025.
The program will operate in six-month cycles, with 20 researchers selected for each cohort. KAIST aims to select the first cohort by the end of 2026 and plans to gradually expand both the scope of support and donor participation.
The program is meaningful in that, amid the rapid spread of generative AI across research settings, it lays the groundwork for KAIST researchers to be early adopters of cutting-edge AI tools and apply them to their research. In particular, the program is expected to foster a new research culture in which master’s and doctoral researchers actively incorporate AI agents into their research workflows and share their experiences with one another.
“I sincerely thank alumnus Beomgyu Lee for once again making a meaningful contribution in support of junior researchers,” said KAIST President Choongsik Bae. “His generous commitment will provide a strong foundation for researchers to actively use generative AI and pursue new research challenges. KAIST will continue to provide sustained support so that researchers can produce creative and innovative outcomes in the best possible environment during this era of AI transformation.”
“AI agents have already become a new standard across industry, and I was concerned that researchers who will lead the development of future technologies were unable to make full use of them because of the associated costs,” said Beomgyu Lee. “I hope this support will help researchers freely use cutting-edge AI tools to help produce world-class research outcomes.”
Professor Sukyoung Ryu from the KAIST School of Computing said, "I am deeply grateful to alumnus Beomgyu Lee for continuing to support junior researchers for a second year in a row." She added, “As generative AI agents are significantly enhancing research productivity and transforming approaches to problem-solving, I hope this program will encourage researchers to actively use the latest AI tools and foster a new culture of sharing their experiences.”
Jae-Gil Lee, Dean of the KAIST School of Computing, said, “We will actively support training and regular seminars so that researchers across all KAIST departments—not just the School of Computing—can use the latest generative AI agents in their research, allowing AI-driven research innovation to spread across the university.”
2026.07.16 View 94 -
KAIST Develops Robot That Judges Its Surroundings and Walks, Runs, and Jumps Like an Animal
An era in which robots decide "how to walk" on their own has arrived. A four-legged robot has been developed that, much like a person or an animal, autonomously chooses the appropriate gait strategy for its surroundings — changing its gait on stairs, leaping over gaps, and keeping its balance on forest trails.
KAIST (President Choongsik Bae) announced on the 16th of July that a research team led by Professor Hae-Won Park from the Department of Mechanical Engineering has developed a core control technology for four-legged robots that lets a single controller select and switch in real time among walking, running, jumping, and other locomotion skills, allowing the robot to move quickly and stably, even in real outdoor environments.
Four-legged robots move on four legs, giving them an advantage over wheeled robots on rough terrain. But in real outdoor settings, obstacles such as stairs, ledges, stepping stones, gaps, and tree branches appear one after another in different forms, meaning the ability to simply walk and run fast is not enough.
Existing four-legged robots have excelled at running quickly across flat ground or clearing simple obstacles, but they have struggled to maintain both speed and stability in real-world environments where obstacles combine in complex ways. Because walking, running, jumping, and other gaits had to be controlled individually, the robots were also limited in how naturally they could switch between them as conditions changed.
To overcome these limitations, the research team developed a new learning-based control technology called APT-RL (Action Pretrained Transformer-based Reinforcement Learning).
APT-RL is a control technology designed to enable a robot to first learn a range of locomotion skills — such as walking, running, and jumping — and then freely combine and transition among them in real-world environments as the situation demands.
Rather than filming the movements of real people or animals, the team generated 15.5 hours of training data covering a variety of gaits using computer simulations alone, in just eight minutes. That data was used to teach the robot basic movement capabilities, drawing on robot dynamics (a mathematical model of how a robot moves) and trajectory optimization (a technique for calculating the efficient path of movement). The approach is far faster and more efficient than earlier methods that relied on motion capture, a technology that records human or animal movement using sensors.
The team then applied reinforcement learning — an artificial intelligence technique in which an agent learns optimal behavior through repeated trial and error — so the robot could autonomously select and switch gaits suited to complex three-dimensional terrain such as stairs, ledges, and gaps. Finally, the team combined a depth camera (which measures the distance to objects in order to obtain three-dimensional information) with LiDAR (Laser Detection and Ranging, a sensor that uses lasers to measure the distance and shape of the surrounding environment in three dimensions), enabling the robot to recognize its surroundings and target speed in real time and choose the most appropriate walking strategy.
The team tested the control technology on its own four-legged robot, 'KAIST HOUND.' The experiments were conducted not only on an indoor obstacle course but also in real outdoor environments, including KAIST’s campus and forest trails.
KAIST HOUND moved stably across urban terrain that included stairs, grass, and slopes, as well as irregular natural terrain such as fallen trees, exposed roots, and paths covered in fallen leaves, switching gaits in real time to match the conditions. In rugged terrain with obstacles, the robot reached a peak instantaneous speed of six meters per second (about 22 kilometers per hour), demonstrating that it can achieve both fast movement and stability in real outdoor environments.
The experiments showed that KAIST HOUND autonomously selected and switched between a trot (alternating diagonal legs) and a bound (a leaping gait using the front and back leg pairs together) depending on the terrain and target speed, and that it could integrate walking, running, jumping, and ledge-clearing into a single controller.
Professor Hae-Won Park said "We expect this to become a foundational technology that expands the potential uses of physical-AI-based walking robots in rugged environments such as disaster sites, defense missions, and industrial facility inspections."
Jun-Gill Kang (affiliated with the Agency for Defense Development (ADD) at the time of the research) and Jaehyun Park, a Ph.D. candidate in KAIST's Department of Mechanical Engineering, are co-first authors of the study. Professor Hae-Won Park and Professor Seungwoo Hong from Korea University are co-corresponding authors. The research was selected as the cover paper for the July issue of Science Robotics, the world's leading academic journal in robotics, and was published on July 15 (U.S. Eastern time).
Paper title: Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
DOI: 10.1126/scirobotics.adz7397
Authors: Jun-Gill Kang (the Agency for Defense Development at the time of the research, co-first author), Jaehyun Park (KAIST, co-first author), Hae-Won Park (KAIST, corresponding author), Seungwoo Hong (Korea University, corresponding author)
Related Video: https://drive.google.com/drive/folders/1306_hddGZGh7xwvWFc4B-9lLXwYisirN
This research was supported by funding from the Ministry of Trade, Industry and Resources (MOTIR) and the Korea Planning & Evaluation of Industrial Technology (KEIT) (RS-2024-00427719), as well as by the Agency for Defense Development's Future Challenge Defense Technology R&D program (912768601).
2026.07.16 View 198 -
KAIST Develops Key Technology to Make Personalized AI Safer
“Create an AI assistant trained only on our company’s documents.”
The era of building “personalized AI” by training AI models on individual or corporate documents and data is beginning. However, while such customization can improve task performance, it can also weaken the model’s existing safety safeguards. KAIST researchers have developed a core AI technology that preserves customized performance while further strengthening safety.
KAIST (President Choongsik Bae) announced on the 15th of July that a research team led by Professor Changick Kim from its School of Electrical Engineering has developed “Buffer-and-Reinforce,” a training framework for safe fine-tuning that prevents safety degradation when large language models (LLMs), such as
ChatGPT, are retrained on data from individuals or companies to better suit their needs.
Until now, one of the biggest challenges in the era of personalized AI has been that fine-tuning improves a model’s ability to perform new tasks, but can also weaken its existing safety rules. The research team focused on prior findings showing that, counterintuitively, fine-tuning an AI model while it is in a temporarily jailbroken state — a state in which it may respond even to dangerous requests it would normally refuse — does not significantly compromise its safety.
The team then devised a new approach in which this jailbroken state is not used in actual services, but is applied only temporarily during the fine-tuning process through a buffering module called “BufferLoRA,” which is removed after training.
The research team was the first to clarify why this phenomenon occurs. They found that, in the temporarily jailbroken state, the AI model becomes less easily influenced by harmful information, while still effectively learning the new task abilities desired by the user. In other words, the model can continue learning useful knowledge without additionally absorbing harmful behaviors.
Based on this insight, the team developed a two-stage learning method consisting of “buffering” and “safety reinforcement.”
First, the temporary buffering module, BufferLoRA, is applied to the AI model during user fine-tuning, where it acts as a protective layer that prevents harmful data from directly affecting the base model. Once fine-tuning is complete, this module is removed.
Next, a safety reinforcement module called “ReinforceLoRA” is applied to restore and strengthen the model’s safety. In this process, the team used QR decomposition, a mathematical technique that separates different types of information and selectively reflects only the necessary components. This allowed the model to retain the new functions learned from user data while selectively reinforcing safety.
Simply put, the researchers first placed a temporary protective layer, BufferLoRA, over the AI model so that harmful data could not directly affect it, while allowing the model to learn the necessary task. They then removed the protective layer and applied ReinforceLoRA to strengthen the model’s safety safeguards. As a result, the model maintained its customized performance while achieving even stronger safety.
In experiments, the AI model maintained high safety even in an extreme setting where all user data consisted of harmful questions and answers. After fine-tuning, the rate at which the AI generated harmful responses was about 8%, lower than the roughly 18% observed in the original model that had not been fine-tuned at all. The framework also achieved strong customized performance and state-of-the-art safety without requiring additional safety data during user fine-tuning or significantly increasing computational cost, suggesting its practical applicability to real-world personalized AI services.
Professor Changick Kim stated, “This research provides a key foundational technology that allows anyone to build customized AI with their own data while using it more safely,” adding, “We expect it to contribute significantly to building a trustworthy AI service environment in the era of personalized AI and AI agents.”
This research was led by Seokil Ham, a doctoral student in KAIST’s School of Electrical Engineering, as first author. The paper was selected as a Spotlight presentation at the International Conference on Machine Learning (ICML) 2026, one of the world’s most prestigious conferences in artificial intelligence, an honor given to only about the top 2.2% of all submitted papers, drawing international attention.
※ Paper title: Jailbreak to Protect: Buffering and Reinforcing via Temporary Jailbreaking for Safe Fine-Tuning in Large Language Models
DOI: 10.48550/arXiv.2605.24550
※ Author information: Seokil Ham (KAIST, first author), Jaehyuk Jang (KAIST, second author), Wonjun Lee (KAIST, third author), Changick Kim (KAIST, corresponding author)
※ Related video: https://drive.google.com/file/d/1gfok06dE8699qtiUR7gVsRoVmBGADaWQ/view?usp=sharing
This work was supported by Institute of Information & Communication Technology Planning & Evaluation (IITP) grant funded by Ministry of Science and ICT(MSIT) (No. RS-2025-02215344, Development of AI Technology with Robust and Flexible Resilience Against Risk Factors).
2026.07.15 View 183 -
KAIST Opens the Era of “Space Sensors” with Optical Functions Reconfigurable by Electrical Signals Alone
Until now, satellites and space payloads have required new optical filters and sensors to be designed whenever their missions changed. A future is now on the horizon in which a single ultra-compact optical chip can perform a variety of roles—including those of a thermal imaging sensor, spectrometer, and infrared camera—using electrical signals alone.
KAIST (President Choongsik Bae) announced on 14th of July that a research team led by Professor Hyun Jung Kim from the Department of Aerospace Engineering, in collaboration with a research team led by Professor Juejun Hu at the Massachusetts Institute of Technology (MIT), has demonstrated the first transmissive mid-infrared amplitude-only spatial light modulator based on a scalable two-dimensional, electrically addressable metasurface architecture.
The key achievement of this research is that a single optical chip can perform a variety of sensor functions using electrical signals alone. Previously, new optical filters and sensors had to be fabricated for each new mission. In the future, the technology is expected to enable the realization of “software-defined sensors,” whose functions can be changed without replacing the hardware.
The device developed by the research team is a transmissive mid-infrared spatial light modulator, or SLM, based on a metasurface. A metasurface is an ultrathin optical structure that uses microscopic patterns much smaller than the width of a human hair to freely control the intensity, direction, and wavelength of light.
A spatial light modulator controls the spatial distribution of light on a pixel-by-pixel basis. In the present device, each pixel switches the intensity of transmitted mid-infrared light between two programmed states. The research team succeeded, for the first time in the world, in electrically and independently controlling each individual pixel.
Conventional spatial light modulators face significant limitations in the mid-infrared. Liquid-crystal-based devices suffer from material absorption and relatively slow response, while digital micromirror devices operate in reflection. Transmissive mid-infrared SLMs have therefore remained largely unexplored. This has limited their application to satellite sensors, ultra-compact spectrometers—which analyze light according to wavelength—and adaptive optical systems, which automatically adjust their optical performance in response to changes in the surrounding environment.
To address these limitations, the researchers used GSST—Ge₂Sb₂Se₄Te, or germanium-antimony-selenium-tellurium—an optical phase-change material (PCM) whose light transmittance changes when it receives an electrical signal.
Once GSST receives an electrical signal, it retains its state and continues to maintain the same optical performance even after the power is turned off. This nonvolatile characteristic eliminates the need for a continuous power supply, making the material suitable for satellites and space payloads, where the available electrical power is limited.
As the number of pixels on an optical chip increases, electrical current can flow into pixels other than the selected pixel, causing unintended pixels to operate as well. This is known as the “sneak-path” problem.
The research team solved this problem by integrating a silicon PIN diode into each pixel. A PIN diode is a semiconductor device that allows electrical current to flow only to the intended pixel. This enabled the researchers to accurately select and control only the desired pixels.
Using this approach, the team independently controlled all the pixels in a 6 × 6 pixel array and successfully produced desired optical patterns. The device also maintained stable performance after more than 16,700 switching cycles, demonstrating approximately 13 times greater endurance than previous technology.
The device was fabricated using silicon photonics, a technology that produces optical devices through standard semiconductor manufacturing processes. This makes it relatively easy to scale the technology to larger optical chips containing hundreds, thousands, or even more pixels.
The current device controls only the amount of transmitted light. In the future, however, more sophisticated metasurface designs are expected to enable the technology to develop into “universal reconfigurable optics,” capable of freely controlling the direction and polarization of light as well.
The greatest significance of this research is that it presents a new concept in which “optics, too, can be changed like software.” In other words, the study provides a foundation for programmable optical hardware that could support different sensing functions through reconfiguration rather than hardware replacement. In the future, this is expected to usher in an era of software-defined sensors, in which a single optical chip can perform different functions depending on the situation, serving as a thermal imaging sensor, spectrometer, infrared camera, or optical communication device.
Once commercialized, the technology is expected to make it possible to implement a wide range of optical systems on a single platform. Potential applications include satellites and space payloads, launch-vehicle health diagnostics, thermal monitoring of space stations, measurement of in-space manufacturing processes, infrared imaging, and optical communications.
This research is an achievement that further advances MIT–NASA collaborative research initiated in 2018, when Professor Hyun Jung Kim was working as a researcher at the National Aeronautics and Space Administration (NASA), and subsequently continued at KAIST.
Building on this foundation, KAIST’s STAR Lab and Professor Juejun Hu’s research team at MIT are currently conducting joint research on active meta-optics, silicon photonics, and space sensor systems, with the goal of applying the technology in actual space environments.
The two teams have established a full-cycle international collaborative research framework encompassing material development, chip design and fabrication, sensor-system integration, space-environment verification, and future flight demonstrations.
Professor Kim’s research team is now developing the technology into an operational space sensor. Under the Ministry of Science and ICT’s Young Researcher Program, the team is developing an ultra-precise system for measuring the surface temperature of launch vehicles.
The research is also being expanded through the “Space Services and Manufacturing Research Center” under the Innovation Research Center Program. The team is conducting research to develop the technology into a common optical platform that can be used for space-station thermal monitoring, anomaly diagnosis, measurement of in-space manufacturing processes, and optical communications.
“This research is not simply about creating one more new optical device,” said Professor Kim. “It presents the foundation for an era of software-defined sensors, in which a single optical chip performs a variety of functions depending on the mission.”
“By combining MIT’s nanophotonics technology—which uses nanostructures to control light—with KAIST’s space sensor technology, we plan to develop this technology into an actual space system,” she added. The research was published online in the international journal Nature Communications on July 7.
Paper title: “Two-Dimensional Pixel-Level Addressable Mid-Infrared Metasurface Spatial Light Modulator”
DOI: 10.1038/s41467-026-75346-5
This work was funded by the Air Force SBIR Program under contract FA2394-23-C-5076, the National Science Foundation under awards 2329088 and 2132929, and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2025-00515651 and RS-2025-02213804).
2026.07.14 View 296 -
KAIST Brings the Era of Microbial Cell Factories One Step Closer
The era of "biomanufacturing", in which microbes, not petroleum, produce chemical products, is one step closer. A KAIST research team has analyzed the key challenges limiting the commercialization of biomanufacturing and proposed an AI-driven strategy for industrialization.
KAIST (President Choongsik Bae) announced on the 14th of July that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering has comprehensively analyzed the key bottlenecks to commercializing biomanufacturing and proposed an industrialization strategy and a roadmap for future growth to address them.
Most chemical products today — including plastics, textiles, and pharmaceutical raw materials — are produced from petroleum. But as concerns over carbon emissions and environmental pollution grow, biomanufacturing, which uses microbes to produce chemicals, is drawing attention as a next-generation manufacturing technology. Still, scaling up lab-developed technologies into economically viable mass production at actual factories remains a major challenge.
Systems metabolic engineering, a core technology in biomanufacturing, designs and optimizes microbial metabolic pathways to build "microbial cell factories" that produce desired chemicals. But technologies that show high productivity in the lab often perform worse once moved to industrial settings — productivity drops, production costs rise, and many fail to achieve price competitiveness, ultimately failing to commercialize.
The research team analyzed succinic acid, a bio-based chemical feedstock, and polyhydroxyalkanoate (PHA), a biodegradable plastic, as representative cases illustrating this "gap between the lab and industry," often called the "valley of death."
Succinic acid is a key raw material for producing eco-friendly plastics and various chemical materials. The team explained that for succinic acid to compete with existing petrochemical products, competitiveness depends not just on production volume, but also on raw material and separation/purification costs, the fermentation process, and market size — all of which must be weighed together. The team also suggested that a phased strategy — entering high-value markets such as pharmaceuticals, cosmetics, and food ingredients first — could be a realistic solution.
PHA is a biodegradable plastic that microbes accumulate inside their cells, an eco-friendly material that breaks down naturally in the environment after use. But PHA is currently less price-competitive than conventional plastics due to high production and recovery costs, and its intrinsic material properties pose a separate barrier: the archetypal polymer P(3HB) is highly crystalline, becomes brittle with age, and has a narrow window between its melting and decomposition temperatures, meaning PHAs are generally not suitable as direct "drop-in" replacements.The team found that a phased approach is needed — simplifying the production process and first applying it to high-value fields such as medical applications and food packaging before expanding into general-purpose markets.
The team predicted that artificial intelligence will become a key to industrializing biomanufacturing going forward. AI can optimize the entire biomanufacturing process — from enzyme and microbial design to digital twins that virtually simulate production processes, and technologies that simultaneously analyze economic feasibility and environmental impact. The team explained that this can shorten development timelines, reduce production costs, and increase the likelihood of successful commercialization.
The team also proposed that techno-economic analysis (TEA) and life cycle assessment (LCA) should be applied as design criteria from the earliest stages of research, rather than as evaluations conducted only after research is complete. The team further emphasized that supply chain resilience — accounting for raw material availability and shifts in the international landscape — should be considered a new design standard for biomanufacturing.
This study is significant not for developing a new production technology, but for comprehensively analyzing the conditions for successful biomanufacturing industrialization and presenting an industrialization roadmap spanning the entire cycle — from securing raw materials to microbial design, fermentation, separation and purification, and market entry. The team expects the study to accelerate the commercialization of the bio-based chemical industry and, over the long term, contribute to shifting the petroleum-centered chemical industry toward an eco-friendly bioeconomy.
The paper, with Ji Yeon Kim and Hye Eun Yu as co-first authors, both Ph.D. candidates in KAIST's Department of Chemical and Biomolecular Engineering, was published online on May 30 in the international journal Nature Communications.
※ Paper title: Beyond petrochemicals: challenges and opportunities in industrial-scale biomanufacturing
※ DOI: 10.1038/s41467-026-73835-1
※ Authors: Ji Yeon Kim (KAIST, co-first author), Hye Eun Yu (KAIST, co-first author), Min Ho Kim (KAIST), Sang Yup Lee (KAIST, corresponding author)
This research was supported by the National Research Foundation of Korea, funded by the Ministry of Science and ICT, through the “Development of Platform Technologies of Microbial Cell Factories for Next-Generation Biorefineries” project (Project No. 2022M3J5A1056117) and the “Development of Advanced Synthetic Biology Source Technologies for Leading the Biomanufacturing Industry” project (Project No. RS-2024-00399424).
2026.07.14 View 174 -
KAIST Finds Clue to Solving the “Electrical Bottleneck” in Semiconductors
When the pathways through which electricity flows inside a semiconductor become blocked, device performance declines and power loss increases. A Korean research team has developed a new structure that could resolve this “electrical bottleneck” and, for the first time, directly confirmed that electric charges flow continuously without interruption. This achievement is expected to become a key technology for improving the performance and power efficiency of future semiconductors, including AI semiconductors and ultra-low-power semiconductors.
KAIST announced on July 13 that a research team led by Professor Seungbum Hong from the Department of Materials Science and Engineering, in collaboration with Professor Kibum Kang from the Department of Materials Science and Engineering at KAIST and Professor Sung Beom Cho’s research team at Sungkyunkwan University, has realized a new structure in which electricity flows without obstruction in a two-dimensional material—an ultrathin material only one or two atomic layers thick—that is attracting attention for next-generation semiconductor devices. The team also developed an analytical platform capable of directly observing this charge transport at the nanometer scale.
In semiconductors, contact resistance, which arises at the interface where a metal electrode meets a semiconductor, degrades performance and causes power loss. Especially as semiconductors continue to scale down, the influence of contact resistance becomes even greater, making it one of the most challenging technical bottlenecks in developing next-generation semiconductors.
Instead of attaching a metal electrode on top of a semiconductor as in conventional approaches, the research team continuously formed semi-metallic and semiconducting regions within a single two-dimensional m
aterial. By creating a structure in which the two regions are naturally connected within the same material, the team demonstrated for the first time that current can flow across the boundary without being blocked.
Specifically, the team continuously implemented a semi-metallic region and a semiconducting region within a single thin film of platinum diselenide (PtSe₂), an atomically thin two-dimensional material. By realizing a monolithic structure, in which a single material is formed continuously without interruption, the team proposed a new structure that allows current to flow across the boundary without obstruction.
Using Atomic Force Microscopy (AFM), a microscope that uses a probe to measure surface and electrical properties down to the atomic level, the team directly visualized charge transport inside the thin film at the nanometer scale.
As a result, the team confirmed for the first time that, when current moved from the semi-metallic region to the semiconducting region, the flow continued naturally without an “electrical bottleneck,” such as a blockage or bending of the current path. This is the first experimental demonstration that a monolithic interface does not interfere with current flow.
Furthermore, the team verified device operation by applying an electric field to the semiconducting region. The results confirmed that current flow can be stably controlled in a metal–semiconductor junction structure, demonstrating the potential of the structure for next-generation electronic devices.
This study presents a source technology that can dramatically reduce contact resistance in next-generation semiconductor devices based on two-dimensional materials. It is expected to be widely applicable to the development of future semiconductor technologies, including AI semiconductors, ultra-low-power semiconductors, and next-generation logic semiconductors.
The study was co-first-authored by Yeongyu Kim, a Ph.D. candidate and Dr. Minseung Gyeon from the Department of Materials Science and Engineering at KAIST; and Ji Hoon Hong, a Ph.D. candidate at Sungkyunkwan University. The work was published in the July 2026 issue of Matter, an international journal in the field of materials science.
※ Paper title: Nanoscale imaging of charge transport across the semimetal-semiconductor interface in monolithic platinum diselenide
DOI:https://doi.org/10.1016/j.matt.2026.102873
This research was supported by the STEAM Research Program and the Nanomaterials Technology Development Program of the Ministry of Science and ICT and the National Research Foundation of Korea.
2026.07.13 View 301 -
KAIST Develops AI Technology to Detect Early Warning Signs of Cerebrovascular Disease at Home
Cerebrovascular disease can lead to serious aftereffects if treatment is delayed, but it is difficult to detect before symptoms appear. KAIST researchers have developed an AI technology that analyzes real-life daily activity and environmental data from older adults to identify digital behavioral markers of cerebrovascular disease risk based on subtle changes at home.
KAIST (President Choongsik Bae) announced on the 12th of July that a research team led by Professor Lisa Lim from the Department of Civil and Environmental Engineering, in collaboration with Professor Jo Woon Chong from the School of Electronic and Electrical Engineering at Sungkyunkwan University (President Ji-Beom Yoo) and Professor Kyung-Hee Cho from the Department of Neurology at Korea University Anam Hospital (President Dongwon Kim), has developed an AI framework that uses long-term lifelog data collected in the homes of older adults to identify the prodromal phase of cerebrovascular disease and assess imminent diagnostic risk.
The study was based on lifelog data from 1,224 older adults collected by LivOn Care Co., Ltd. in real residential environments. The research team analyzed a total of 13,362 two-week lifelog samples, demonstrating the possibility of detecting early warning signs through subtle changes in daily life, rather than relying only on the conventional approach of treating the disease after it has already occurred.
The research team developed AI technology that identifies cerebrovascular disease risk stages by analyzing daily activity, sleep, circadian rhythm, and indoor environmental information, together with age and chronic disease data. This shows that changes in everyday living patterns, which are difficult to capture through hospital examinations alone, can serve as important clues for detecting early risk signals of cerebrovascular disease.
The team also succeeded in assessing whether a cerebrovascular disease diagnosis was approaching by analyzing changes in lifestyle patterns over time. When lifelog data from within four weeks before diagnosis were classified as the “imminent diagnostic risk period” and data from 12 weeks before diagnosis were classified as the “non-imminent period,” the AI distinguished between the two periods with a high accuracy of 96.53%. This result suggests that even before a hospital visit, small changes in daily life may help identify whether the risk of cerebrovascular disease has increased.
Another key feature of this study is that the AI does not simply determine whether a risk exist, but also applies explainable AI to identify the lifestyle patterns and environmental factors behind its judgment.
The analysis showed that older adults in the prodromal phase of cerebrovascular disease tended to show frequent continuous activity between 10 p.m. and 2 a.m., a time when the body would normally be preparing for sleep. In other words, irregular daily rhythms, such as delayed sleep onset and a reduced distinction between day and night activity, were closely associated with prodromal signals of cerebrovascular disease.
The researchers also found that as the time of diagnosis approached, the frequency of continuous activity during the evening period from 6 p.m. to 10 p.m. noticeably decreased, while inactive time increased. Low indoor humidity, indicating a dry indoor environment, also emerged as an important factor in identifying an imminent diagnostic risk.
The research team expects this technology to be used as a digital healthcare tool that can objectively monitor the health status of older adults who may have difficulty clearly describing their own condition, while providing useful early warning indicators to medical professionals and caregivers.
However, the team explained that this study does not predict the exact onset of cerebrovascular disease or replace clinical diagnosis. Rather, it is a supportive technology intended to aid prevention and early medical consultation, and prospective validation in larger patient groups will be necessary before actual clinical application.
Professor Lisa Lim said, “The key point of this study is not that AI should replace a hospital diagnosis, but that it can first detect risk signals in small lifestyle changes at home and help connect patients to medical care at the right time,” adding, “We expect this technology to contribute to a shift from a healthcare system that treats disease after it occurs to one that supports prevention and early intervention.”
This study, with KAIST Dr. Jeongyeop Baek as the first author, was published on June 2 in npj Digital Medicine, a leading international journal in digital healthcare published by Nature Portfolio, with an impact factor of 15.1 and ranked in the top 0.3% of JCR journals.
※ Paper title: AI home monitoring for behavioral markers of cerebrovascular disease
DOI: https://doi.org/10.1038/s41746-026-02836-7
This work was also supported by the National Research Foundation (NRF) grant funded by the Korea government (Ministry of Science and ICT) (RS-2025-16068234).
2026.07.13 View 324 -
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 319 -
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 491 -
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 567 -
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 567 -
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 596