KAIST

Clues to stress can be hidden in everyday habits such as chatting, searching, and making payments on a smartphone. KAIST researchers have developed a technology that analyzes several routine behaviors repeatedly performed within apps to identify a person’s daily stress state. KAIST (President Choongsik Bae) announced on October 15 that a research team led by Professor Uichin Lee from the Department of AI Computing developed “SHIRBT-MMF,” a technology that automatically identifies repeated smartphone behaviors and uses factors such as the duration and frequency of each behavior and text-input patterns to determine daily stress status. Previous smartphone-based stress research has mainly analyzed factors such as screen time, the number of app launches, time and frequency of use by app category, and typing behavior. However, when the apps being used or their interfaces change, the data being analyzed also change, which can reduce the stability of models designed to track stress over long periods. The research team therefore focused on “what a person does” rather than “which app they use.” Even if a user switches from KakaoTalk to LINE or WhatsApp, the activity itself remains “messenger chatting.” In other words, the analysis is based on the purpose of the behavior rather than the name of the app. The team defined these repeated behaviors as “Smartphone Human Interaction Routine Behavior Tasks (SHIRBTs).” Using its independently developed “Multi-Level Sequential Pattern Mining (ML-SPM)” technique, the team identified recurring screen-transition patterns across multiple days and then used a large language model (LLM) to assign easily understandable behavior labels such as “messenger chatting,” “web browsing,” “financial payment,” and “reading/writing email.” In evaluations of whether the same behavior could be consistently classified across different email, web browser, and messenger apps, the system achieved an accuracy of more than 95%. To protect privacy, the system did not store the actual text or message content entered by users, recording only behavioral information such as whether text was entered, how often it was entered, and screen transitions. Information about message recipients or call partners was also processed using hashing, which converts the original information into values that do not directly reveal it. The research team analyzed real-life data from 26 university students and office workers in their 20s and 30s. Among participants for whom both self-reported stress assessments and smartphone records were available, 16 short-term participants provided an average of 30.5 days of data, while 10 long-term participants provided an average of 105.1 days. Based on the participants’ daily self-assessments, the researchers built personalized models that distinguished between days with and without stress. A total of 29 types of repeated behaviors were identified in participants’ smartphone use, with each person routinely repeating an average of about seven behaviors. The research team used personalized artificial intelligence models trained on these behavioral patterns to determine whether a person was experiencing stress on a given day. The best-performing model achieved an average accuracy of 75.9% for short-term participants and 73.8% for long-term participants, meaning it was correct roughly three out of four times. These results were 5.5 percentage points and 3.5 percentage points higher, respectively, than the conventional approach of analyzing app usage using the same AI technique. Performance could also be maintained even when the number of behaviors used in the analysis was reduced. With the same personalized model, using only two behaviors, “messenger chatting” and “financial payment,” or even analyzing “messenger chatting” alone, produced higher average classification accuracy than the conventional method. In the long-term data, analytical indicators based on repeated behaviors also changed less over time and maintained a more stable structure than those used in the conventional approach. In this experiment, the researchers collected a broad range of smartphone usage records in order to compare their method with conventional approaches. In the future, the team expects that once an individual’s repeated behaviors have been identified, focusing data collection and analysis on those relevant behaviors could reduce the burden of data collection. The study suggests the potential for developing digital mental health monitoring technology that makes it easier to understand which behaviors were used in classification and remains relatively stable even when the app environment changes. Professor Uichin Lee of the KAIST AI Computing Department said, “We viewed the smartphone not simply as a device for measuring usage time, but as a space in everyday life where an individual’s communication, information seeking, and financial activities are repeatedly carried out,” adding, “This study provides a foundation for converting complex smartphone records into human-understandable behavioral units and offering long-term mental health tracking more reliably.” However, this study used associations between behavior and stress and did not demonstrate a causal relationship showing that a particular behavior causes or reduces stress. Based on findings from Android smartphone users in their 20s and 30s, the research team plans to expand the study to a broader range of age groups, occupations, and smartphone environments and verify its long-term stability. In this study, Hansoo Lee participated in the study as the first author, Professor Uichin Lee as the corresponding author, and researchers Taehyeon Park and Youngji Koh, along with Professor Jae-Gil Lee of the School of Computing, as co-authors. The research was published in December 2025 in the international journal Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. The study is scheduled to be presented at ACM UbiComp/ISWC 2026, which will be held in Shanghai, China, from October 13 to 15, 2026. Paper title: Leveraging Smartphone Human Interaction Routine Behavior Task Mining and Modeling for Daily Stress Monitoring, DOI: https://doi.org/10.1145/3770644 Author information: First author (Hansoo Lee), co-authors (Taehyeon Park, Youngji Koh, Jae-Gil Lee), corresponding author (Uichin Lee) This research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) ICT Convergence Digital Inclusion Technology Development project and the National Research Foundation of Korea (NRF) Sejong Fellowship project, funded by the Ministry of Science and ICT.

Some information, such as exam questions or bid prices, must remain unreadable until an agreed time, even if it has already been delivered. KAIST researchers have used changes in the electric current inside a semiconductor to develop encryption in which information is revealed only after a set amount of time has passed, or can be read correctly only at a specific time. In addition to the key needed to restore the original information, the technology adds a second condition: when the information is read. KAIST (President Choongsik Bae) announced on October 14 that a research team led by Professor Kyung Min Kim from the Department of Materials Science and Engineering has developed a dual-state programmable oxide transistor (DUPOT), a single device in which long-term and short-term memory can be controlled independently. The team then used the transistor to implement time-based cryptography. A transistor is a small semiconductor component that controls the flow of electric current. The DUPOT is made from oxide materials, and its two electrical states can be independently set by applying appropriate voltages. The term “programmable” refers to this ability to set the device to a desired state. Encryption methods that use time as a condition for accessing information have been studied before. What sets this study apart is that the time at which information can be read is determined by physical changes within the semiconductor itself, rather than by external clocks or software alone. To achieve this, the researchers implemented two types of memory in a single transistor: long-term memory, which is retained for a long period, and short-term memory, which changes over time. In this context, memory refers to a device’s ability to retain a state that has been changed by an applied voltage. The team used the long-lasting state to set the reference for the information and the time-varying state to control when the target information appears. Conventional memory transistors store information mainly by changing the threshold voltage, the voltage at which current begins to flow. The DUPOT, by contrast, can independently control both the threshold voltage and the saturation current, the level of current maintained under fixed operating conditions. As a result, the voltage at which current begins to flow and the amount of current that flows can be set separately. The two states are produced by different mechanisms. When a positive voltage is applied to the gate, the terminal that controls the current, electrons become trapped in hafnium oxide (HfO₂), an insulating layer that does not easily conduct electricity. This shifts the threshold voltage. Because this state is retained for a long time, it serves as long-term memory. When a negative voltage is applied to the gate, oxygen ions (electrically charged oxygen particles) move to the interface between the insulating layer and the channel through which current flows, which reduces the saturation current. Once the voltage is removed, the oxygen ions gradually redistribute and the current recovers. The team used this change as short-term memory. Using several transistors, the researchers configured how the current changed over time and set a readout criterion for each device to demonstrate two encryption methods. In time-release encryption, information appears only after a certain amount of time has passed. In the experiment, the difference in current between the target pattern and the background grew over time, and the letter “T” became readable after 8.4 seconds. In time-bound encryption, the correct information can be read only within a specified time window. In the experiment, a decoy pattern, the letter “K,” appeared at 1.5 seconds, while the target letter “T” appeared at the designated time of 37 seconds. The same array therefore displays different information depending on when it is read. These time points were chosen for the experiment. The team also proposed an encryption method for cloud environments, in which data are stored on remote servers accessed over the internet. In this method, different time conditions are applied to the ciphertext stored in the cloud and to the decryption key held by the user Ciphertext is data that has been converted into a form in which the original information cannot be recognized, and a decryption key is the information needed to convert it back. In a demonstration using the device array, the researchers restored the original information by combining the ciphertext and the key that matched each time condition. The study shows that a semiconductor can do more than store information: it can also determine when that information is read. The technology could be used to protect information that must not be disclosed before a certain time or that must be readable only under specific time conditions. However, the technology cannot revoke access to copies of information that have already been obtained. “The significance of this work is that long-lasting memory and time-varying memory can each be controlled independently within a single transistor and used for time-based encryption,” said Professor Kyung Min Kim. Kim added that the team plans to build on the time-dependent changes of the semiconductor itself to develop the technology further for cloud and smart-device security, as well as for a range of information-processing technologies. The current study has demonstrated the feasibility of time-based encryption at the array level. The team plans to lower the operating voltage and improve device-to-device uniformity and consistency over repeated operating cycles, so that the devices can work stably in a variety of environments. The researchers then aim to extend the technology to time-based security and to neuromorphic devices, which mimic how neurons in the brain work and connect. The study, with Ph.D. candidate Huisu Noh of the Department of Materials Science and Engineering as first author and Professor Kyung Min Kim as corresponding author, was published online in Advanced Materials on September 10, 2026. Paper title: Dual-State Programmable Oxide Transistor for Time-Based Cryptography, DOI: 10.1002/adma.74951 This research was supported by the Next-Generation Intelligent Semiconductor Technology Development Program of the National Research Foundation of Korea (NRF) (RS-2025-02433006).

Two-dimensional (2D) semiconductors, only a few atomic layers thick, are a promising material for making AI chips faster and more energy efficient by stacking additional semiconductor layers on top of existing computing circuits. However, when crystals form at multiple random locations during growth, it is difficult to produce high-quality semiconductors at the desired positions. A KAIST joint research team has developed a technology that sets in advance where a crystal begins to grow and allows only one crystal to grow at that position. By controlling the starting point of semiconductor growth, the work addresses a key challenge in realizing next-generation stacked semiconductors. KAIST announced that a research team led by Professor Kibum Kang from the Department of Materials Science and Engineering, in collaboration with UNIST, Hanyang University, and TDS Innovation (Co-CEOs Kibum Kang and Sanghyun Mo), has developed a new growth technique that precisely controls the location of nucleation, the stage at which 2D semiconductor crystals first form, using chemical species released from an oxide barrier. When AI generates responses or processes large amounts of data, time and power are consumed not only by computation but also by moving data within the chip. Stacking the devices used for computation and data storage vertically, so that they are placed close together, shortens the distance that data must travel and can reduce processing delays and power consumption. This requires a technology that forms high-quality semiconductors directly at the required positions on top of circuits that have already been fabricated. Deposited semiconductors, in which devices are made by forming a thin film of semiconductor material directly on the required surface, are a promising technology for building stacked chips. 2D semiconductors are particularly well suited for stacking thin device layers because they retain the electrical properties of a semiconductor while being only a few atomic layers thick. The challenge is that, as a thin semiconductor film grows, the positions where crystals begin to form are inherently random. A crystal is a structure in which the atoms that make up the semiconductor are arranged in a regular, repeating pattern. When crystals grow separately from several locations and meet, grain boundaries form between them. These boundaries hinder the movement of electrons and can degrade the quality of the semiconductor. Area-selective growth techniques, which define in advance the regions where a semiconductor will grow, already existed. However, it has been difficult to control exactly where crystals first form within those regions and how many of them form. The research team achieved deterministic nucleation, in which nucleation that previously occurred at random takes place one at a time at defined positions. In addition to defining the region where the semiconductor will grow, the team also determined the point within that region where growth begins. The researchers named the technique etching-flux-mediated single-centered nucleation (EF-SCN). The key element is oxygen released from an oxide barrier, such as hafnium oxide (HfO₂), that surrounds the growth region. This oxygen acts as an etching flux that suppresses the formation of crystal nuclei of molybdenum disulfide (MoS₂), a representative 2D semiconductor. Etching refers to chemically removing material, and flux refers to the flow of a specific chemical species that is transported and supplied in a way that affects nearby chemical reactions. The suppressing effect is strong at the edges near the oxide barrier and becomes weaker toward the center, which is farthest from the barrier. Using this difference, the team caused a single crystal to begin growing at only one point in the center. While the crystal that started at the center continued to grow, the formation of new crystals around it was also suppressed, so that the single crystal could grow larger. In experiments, the team grew one MoS₂ single crystal at a designated central position in more than 99% of 400 different patterns. A single crystal has one continuous atomic arrangement, and because it reduces the boundaries that form where multiple crystals meet, it is advantageous for producing high-quality semiconductors. Using MoS₂–MoSe₂ lateral heterostructures grown with this method, the team also deposited a thin film of ruthenium (Ru) metal selectively on the molybdenum diselenide (MoSe₂) regions only. The researchers further analyzed the principle behind this position control through growth experiments and simulations. The significance of this work is that it turns the starting point of semiconductor thin-film growth, which had been determined at random, into a process parameter that can be designed. Instead of fabricating 2D semiconductors elsewhere and then transferring them, this approach could lead to a manufacturing technology that grows high-quality semiconductors directly at the required positions on a chip. Deposited semiconductors are expected to be adopted in the 2030s to improve the performance and lower the power consumption of AI chips. If applied to stacked AI chips, the technology could reduce the time and energy spent on moving data, which would shorten the time users wait for AI responses and reduce the power demands of data centers. “In thin-film growth, where a crystal forms is inherently a matter of probability, and the key significance of this study is that it allows that starting point to be determined at a desired position,” said Professor Kibum Kang. Professor Kang added that the technology will serve as the basis for stacked-channel processes, in which semiconductor devices are stacked vertically, by growing high-quality 2D semiconductors directly where they are needed. “In 2D semiconductors, the position where growth begins can change in response to even small chemical changes in the surroundings,” said Dr. Jeongwon Park, a co-first author. Park explained that the significance of the study lies in using this property to control where the semiconductor begins to grow. Dr. Park and master’s student Jiyun Kim from the KAIST Department of Materials Science and Engineering, Sumin Kang, who received a master’s degree from the same department, integrated M.S.–Ph.D. student Jieun Oh from the Department of Materials Science and Chemical Engineering at Hanyang University, and integrated M.S.–Ph.D. student Youngmin Sunwoo from the Graduate School of Semiconductor Materials and Devices Engineering at UNIST participated as co-first authors. The findings were published in Nature on October 8. Paper title: Spatially deterministic nucleation of 2D semiconductors by etching flux DOI: 10.1038/s41586-026-11095-1 This research was supported by the Nano and Material Technology Development Program and the Basic Science Research Program of the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT). Additional support was provided by the International Cooperation Program for Source Technology Development, the Development of vdW Materials and Process Technologies for Ultra-High-Density Semiconductors Program, the Next-Generation Intelligent Semiconductor Technology Development Program, the Ministry of Trade, Industry and Energy (MOTIE), the Korea Institute for Advancement of Technology (KIAT), the Commercialization Promotion Agency for R&D Outcomes (COMPA), the Innopolis Foundation, the Ministry of SMEs and Startups (MSS), and NVIDIA.

Researchers have developed a technology that lets a user move a small robot by bending and straightening a finger. The user can also pick up and move objects while checking the images and contact information that the robot sends back. The system combines artificial muscles, a camera, and tactile sensors in a small soft robot modeled on a beetle. It is expected to be used in remote tasks, such as inspecting narrow spaces that are difficult for people to reach and retrieving small objects. KAIST (President Choongsik Bae) announced on October 13 that a research team led by Professor Sang Ouk Kim from the Department of Materials Science and Engineering has developed a small soft robot called “BeetleBot.” The team worked with the research groups of Professor Wei Gao at the California Institute of Technology (Caltech) and Professor Hirotaka Sato at NTU. When people grasp an object, they use their eyes to find where it is and the sensation in their fingertips to check the contact. A robot that adjusts its work to its surroundings also needs its movement and sensing functions to be connected. Soft robots are built from soft, deformable materials. In these robots, however, actuation, sensing, and control are often built as separate parts or depend on complex devices. This has made it difficult to integrate several functions into a small robot. The researchers designed the robot to walk and grip objects using artificial muscles that contract in a similar way to biological muscles. They linked these muscles with a camera, a pressure sensor, and a stretchable sensor patch worn on the finger. With this setup, the user can control the robot while checking its surroundings and its contact with objects. The team first analyzed how the leg muscles of a beetle relate to the movement of its joints. They used the results to decide where to place BeetleBot’s artificial muscles and how to design its legs. The artificial muscles were made by 3D printing a liquid crystal elastomer (LCE), a polymer that contracts when heated. They were combined with an actuation module that produces heat from electricity. The muscles in each part of the robot can be operated independently, and the order in which they operate can be adjusted. This allows the robot to walk forward and turn left or right. The researchers also built a soft gripper based on the mandibles that beetles use to hold their prey. The gripper combines artificial muscles with a capacitive pressure sensor, so it can grip an object and detect the contact. When the artificial muscles contract, the gripper closes around the object. The pressure sensor then detects the pressure applied during contact. When the object presses on the sensor, the distance between its internal electrodes changes, and so does its capacitance. The sensor uses this change to determine the contact state. To control the robot, the team used a stretchable sensor patch whose electrical resistance changes as the finger moves. When the finger bends and stretches the patch, the conductive paths formed by carbon nanotubes (CNTs) inside it change, which changes the resistance signal. The researchers sorted these signals into four commands: move forward, turn left, turn right, and grip an object. The commands are sent to BeetleBot over Bluetooth Low Energy, and the robot carries out the matching action. The user views the robot’s surroundings through a small onboard camera and checks contact with objects using data from the pressure sensor in the gripper. Based on this information, the user decides on the next action and sends a command with a finger movement. In this way, the system provides bidirectional feedback. Commands go from the user to the robot, and the information the robot detects goes back to the user. Using this control method, the researchers showed that BeetleBot could avoid obstacles, approach a target object, and then grip and carry it. By connecting artificial muscle-based walking and gripping, visual and tactile sensing, and user control in a single system, the study shows that small soft robots can carry out practical tasks. The results are expected to support the development of soft robots for remote tasks, such as checking conditions and retrieving small objects in spaces that are hard for people to access. Before the robot can be used more widely, for example in environmental exploration or industrial equipment inspection, the actuation speed, energy efficiency, and power supply of the artificial muscles will need to be improved. Its performance will also need to be tested in real working environments. “This study expands the potential uses of soft robots by connecting the movement of artificial muscles, sensing, and user control in a single system,” said Professor Sang Ouk Kim. He added that he expects the joint research with Caltech and other partners to contribute to soft robots that can work in spaces that are difficult for people to access. “A key feature of this work is the bidirectional feedback, which links the information detected by the robot to the user’s next action,” said Professor Inho Kim of Sungkyunkwan University, the first author. He added that he plans to increase the actuation speed and energy efficiency of the artificial muscles and improve control methods that use sensory information, so that the robot can be developed into a soft robot that works in a variety of environments. Professor Inho Kim earned his Ph.D. from the KAIST Department of Materials Science and Engineering. He is now an assistant professor in the Department of Chemistry at Sungkyunkwan University, and he took part in the study as first author while working as a postdoctoral researcher at Caltech. Professor Sang Ouk Kim of KAIST and Professor Wei Gao of Caltech are co-corresponding authors. The results were published in Science Advances on August 19, 2026. Paper title: BeetleBot: An integrated bioinspired soft robot for multimodal sensing and adaptive interaction, DOI: 10.1126/sciadv.aeg8849 This research was supported by the Heritage Medical Research Institute, the Meta Caltech Research Award, the National Research Foundation of Korea (RS-2024-00411904, RS-2025-00521316, RS-2025-25442577), and the Singapore Ministry of Education (RG140/20, RG82/24).

What happens at the nanoscale when information is written to memory? Researchers at KAIST have shown how tiny regions with a new polarization direction form while previously formed regions continue to expand in a promising ferroelectric material. By linking these nanoscale changes to electrical measurements, the team developed a model that captures both processes, offering a basis for designing faster and more stable memory. KAIST (President Choongsik Bae) announced on October 8 that a team led by Professor Seungbum Hong from the Department of Materials Science and Engineering had identified how information is recorded in hafnium zirconium oxide (HZO), a promising material for next-generation memory. The study was conducted in collaboration with Professor Byung Jin Cho’s team at KAIST’s School of Electrical Engineering and researchers at NaMLab/TU Dresden in Germany. The research team focused on ferroelectrics. Ferroelectric materials can retain their electrical polarization after an applied voltage is removed. Reversing this orientation allows the material to store information as 0s and 1s. The electrical state in which positive and negative charges inside a material are aligned in a particular direction is called polarization. HZO combines hafnium oxide, a material widely used in semiconductor manufacturing, with zirconium oxide. Its compatibility with existing semiconductor manufacturing processes makes it attractive for nonvolatile memory, which retains data without power. When a voltage is applied to a ferroelectric, tiny regions called domains, each with a uniform polarization direction, begin to change. As new domains form and existing domains expand into the surrounding area, the overall polarization direction reverses and information is recorded. Researchers have traditionally described this switching behavior using models that emphasize either nucleation, the formation of new domains, or growth, the expansion of existing domains. In hafnia-based thin films such as HZO, however, switching occurs across many nanometer-sized crystal grains. Nucleation and growth interact with grain boundaries and other structural features, making it difficult for models that emphasize either process alone to fully capture the observed behavior. Using high-resolution piezoresponse force microscopy (PFM), the researchers directly observed how nanometer-scale domains (one nanometer is one billionth of a meter) change as the applied voltage increases. They then compared these observations with the electrical switching behavior of the full devices. The combined results showed that new domains continued to form while existing domains expanded. In other words, information is recorded through two simultaneous processes: new changes begin, and changes already under way spread. To describe this behavior, the team introduced a simultaneous nucleation and growth (SNG) model. The model accounts for both the continuing formation of new domains and the cumulative expansion of those already present, bringing the two processes together in a single framework. The team also linked the nanoscale changes seen under the microscope directly to the electrical behavior of real memory devices. This makes it possible to explain not only how quickly the memory switches, but also where information writing begins and how it spreads. The findings can also be used to improve the performance of next-generation memory. Once researchers understand how domains form and spread, they can adjust a material's structure or manufacturing process so that information is written more quickly and uniformly. This understanding could help researchers improve memory performance by adjusting material structures and fabrication conditions to make switching faster and more uniform. The findings could also guide the development of more reliable, energy-efficient nonvolatile memory and neuromorphic devices for AI. Neuromorphic devices mimic aspects of the brain’s information processing and storage, offering potential applications in low-power AI hardware. “When information is written in a ferroelectric material, which retains its electrical state even without power, small changes begin at multiple sites while those already under way spread into the surrounding regions,” said Professor Hong. “This study reveals how these two processes work together to write information, providing a new basis for designing faster and more reliable next-generation memory.” Batzorig Buyantogtokh, a postdoctoral researcher in KAIST’s Department of Materials Science and Engineering, is the study’s first author. The paper was published in Nano Letters on June 15, 2026, and was selected for a journal cover. Paper: “Nucleation-to-Propagation Switching Modes in Ferroelectric Hf₀.₅Zr₀.₅O₂ Capacitors” DOI: https://doi.org/10.1021/acs.nanolett.6c01580 This research was supported by the National Research Foundation of Korea (NRF), with funding from the Ministry of Science and ICT (MSIT), under grant numbers RS-2026-25487626, RS-2026-25468150, and RS-2023-00272257.

KAIST hosted “Physical AI KOREA 2026” at its Daejeon campus on October 13. At the event, KAIST presented physical AI platform technologies that can be applied not only to manufacturing but also to healthcare, defense, logistics, and everyday life, as well as strategies for research, education, and entrepreneurship that can be utilized by a wide range of companies. Humanoid robots are beginning to surpass human records in robotic competitions modeled after Olympic events such as track and field, and pilot applications have also begun at some manufacturing sites. Expectations for physical AI are growing, but KAIST’s vision extends beyond stadiums and selected factories. KAIST will announce a vision to expand physical AI into technologies used across diverse industries and everyday life and develop it into a future industry for Korea. President Choongsik Bae emphasized that “the key competition in physical AI is not about how closely robots resemble humans, but about how widely we can deploy AI that understands and moves within the physical world across diverse environments,” adding, “True to its reputation for strong fundamentals, KAIST will build foundational technologies for sensing, movement, and cooperation among multiple machines so that they can be used across a wide range of robots and industries. We will also foster the talent and companies that will lead this field.” On the day of the event, President Bae appeared on stage in a wearable robot developed by Professor Kyoungchul Kong’s research team and demonstrated how the movement data measured by the robot could be analyzed. This served as an example of how physical AI can also be used to understand human movement and health. The wearable robot developed by Professor Kong's research team has primarily been studied as a technology for rehabilitation therapy and for helping people with disabilities walk. When combined with physical AI technology, the robot could be used not only as a walking-assistance device but also as a sensing device that monitors a person's movement. President Bae's demonstration will show KAIST's vision that physical AI can extend beyond manufacturing and industrial robots into people's health and daily lives. The event opened with a video tracing the development of robotics research at KAIST, followed by an overview of global trends in physical AI technology. The program featured presentations on platform technologies and demonstrations of robots, including humanoid robots and a robot pilot. Speakers then addressed the challenges of industrializing physical AI and the role KAIST can play, and President Bae presented a vision for building a physical AI industrial ecosystem in Korea. The event  also included a joint Q&A session with the presenters and media interviews. The event went beyond the question of “what kind of robot should we build” and presented KAIST’s answer to “what technologies are commonly needed across different robots and industries.” KAIST plans to combine its accumulated research capabilities in robotics, artificial intelligence, sensors, actuators, and data to present a technological framework for physical AI and identify areas of future focus. At the center of this framework are perceptual intelligence, which enables machines to “sense” the physical world; motor intelligence, which allows them to “move” appropriately according to the situation; and collective intelligence, which enables multiple robots and systems to “make decisions and act together.” Humanoids are one important form through which these capabilities can be realized, but KAIST does not limit the scope of physical AI to humanoids. Perceptual intelligence is technology that measures physical signals such as force, movement, and position, allowing AI to understand its surroundings and the condition of people. Motor intelligence uses this information to generate movements appropriate to the situation, such as walking or grasping objects. Collective intelligence allows multiple robots and sensors to share information and make decisions and take actions together. Rather than concentrating its capabilities on perfecting a single specific robot, KAIST plans to secure platform technologies that can be commonly used across multiple robots and industries. Key platform technologies include actuators that move robots with precision; technologies that measure and process physical data from the real world; data center technologies that manage and operate large-scale data; motor intelligence that autonomously generates movement; and sensory intelligence that understands surrounding environments and people. The event also showcased a variety of implementation examples. Professor Hae-Won Park's research team in the Department of Mechanical Engineering introduced a humanoid robot capable of carrying objects weighing more than 15 kg. Professor Hyunchul Shim's research team in the School of Electrical Engineering demonstrated PIBOT, a humanoid robot pilot that can operate an aircraft, showing the potential for applications in specialized work environments. Professor Hyun Myung's research team, also in the School of Electrical Engineering, introduced autonomous navigation technology that uses cameras and LiDAR to recognize unfamiliar spaces and determine the robot's own position and path. Professor Hyeon-Min Bae's research team in the same school presented technology that combines motors, sensors, and control systems to move robots with precision. Professor Kyoungchul Kong's research team in the Department of Mechanical Engineering introduced wearable robot technology that identifies the user's intended movements and assists walking. Together, the work of these five research teams shows how the technologies robots need to perceive their surroundings, generate force and movement, and carry out real tasks connect with one another. Connecting technologies developed in the laboratory to industry is another major pillar of the vision. KAIST faculty startups including Angel Robotics and Raion Robotics are working to develop related technologies into platforms and apply them in real-world environments. President Bae outlined a strategy that connects the university’s foundational technology research with product development by faculty startups so that physical AI platform technologies can be utilized across a wide range of settings. KAIST is also continuing collaborative research on physical AI that understands human movement. Professors Jung Kim and Kyoungchul Kong serve as co-directors of the Human Physical AI Research Center, which is pursuing related research through collaboration with the NVIDIA AI Technology Center (NVAITC). Alongside technology development, KAIST also plans to build an ecosystem linking talent development, entrepreneurship, and industrial expansion. It will support education and laboratory-based startups for students and young researchers while expanding retraining programs for personnel working in industry. The goal is to ensure that research outcomes do not remain limited to individual experiments but lead to corporate technology development and real-world applications. KAIST plans to continue hosting “Physical AI KOREA” to share progress in technology development and remaining challenges and to develop the event into a platform for collaboration between researchers and companies. The event is open to all members of the public who are interested. Those who wish to attend should register in advance through the online registration page ( https://docs.google.com/forms/d/e/1FAIpQLSdwirZfcNDBxEZogWqpDVgfBKslVRwHuQ-s-2fWtCBZYOTuUQ/viewform). Registration is open until 12 p.m. (KST) on October 12. Because seating is limited and will be allocated on a first-come, first-served basis, registration may close earlier.

Technologies that reduce noise using thin, lightweight structures, make wireless charging devices lighter, and enable robots to navigate and perform tasks autonomously will be on display at the 2026 KAIST Tech Fair. The event will give companies an opportunity to explore technologies developed in KAIST laboratories and discuss potential applications in their products and manufacturing processes directly with the researchers. KAIST announced on October 12 that it will hold the 2026 KAIST Tech Fair on October 13 in conjunction with KES 2026 (Korea Electronics Show) at COEX in Seoul. The event will showcase six technologies with strong commercial potential, alongside products and services from four faculty-founded startups, and offer consultations on technology transfer and joint research. “KAIST’s vision of ‘Beyond Laboratory’ is about ensuring that outstanding research outcomes extend beyond the laboratory to create real value for industry and society,” said KAIST President Choongsik Bae. “We hope this year’s Tech Fair will connect KAIST’s innovative technologies with industry needs and lead to tangible partnerships in technology transfer and joint research that create new value for industry.” KAIST will operate the KAIST Special Pavilion in Hall A at COEX from October 13 to 16. Industry representatives and other visitors will be able to explore the technologies and the faculty-founded startups’ products and services, as well as learn about KAIST’s support programs for technology commercialization. A technology transfer briefing will take place on October 13, starting at 1:30 p.m., on the KES Stage in Hall B. KAIST researchers will present six technologies and explain their potential industrial applications and prospects for commercialization: • Noise reduction technology that uses thin, lightweight structures to reduce noise and vibration (Professor Wonju Jeon, Department of Mechanical Engineering). • Autonomous robotics technology that enables a robot to use its robotic arm to push obstacles out of the way and continue along its path (Professor Sung-Eui Yoon, School of Computing). • Wireless charging component design technology that makes wireless charging devices lighter and more efficient (Dr. Changwoo Lee, Professor In Gwun Jang’s laboratory, Cho Chun Shik Graduate School of Mobility). • Precision optical imaging technology that uses light to capture clear three-dimensional images within tissue over an extended depth range (Professor Hongki Yoo, Department of Mechanical Engineering). • Antibacterial metal filter technology that allows filters to be washed and reused multiple times (Professor Sanha Kim, Department of Mechanical Engineering). • Multicolor reflective material technology that uses microscopic dome-shaped structures to reflect light and produce a variety of colors and images (Professor Shin-Hyun Kim, Department of Chemical and Biomolecular Engineering). Each presentation will include a 15-minute overview followed by a five-minute Q&A session. Industry representatives will be able to ask researchers how the technologies work, what sets them apart from existing approaches, and how they might be applied to their own products or processes. One-on-one technology transfer consultations will also be held at the KES Lounge in Hall B. KAIST will match companies that register in advance with researchers to discuss technology adoption, joint research, further development and commercialization. Four faculty-founded startups will exhibit at the KAIST Special Pavilion, showcasing their main products and technologies and meeting with companies, investors and other industry representatives. DAIM Technology will present simulation technology for unmanned factories and autonomous manufacturing, along with systems for coordinating and controlling multiple robots. MilliTrack will showcase precision location tracking and spatial recognition solutions for industrial environments. A2US will introduce air and water purification solutions based on technology that uses electricity to disperse water into fine droplets. Hypergram will showcase an industrial inspection solution combining AI with hyperspectral imaging, which analyzes light across a broad range of wavelengths. The solution detects contamination and surface defects that are difficult to identify with conventional cameras or the naked eye. In addition to the faculty startup zone, the pavilion will feature an area showcasing technologies with commercial potential, an information area for the KAIST Office of Technology Value Creation, and a Brand KAIST area. Together, these exhibits will illustrate KAIST’s approach to technology commercialization, from identifying promising research outcomes and securing intellectual property rights to technology transfer, startup creation and further commercialization. The KAIST Special Pavilion will also be a stop on the official KES VIP tour, providing an opportunity to introduce KAIST’s research outcomes and faculty-founded startups to representatives of major institutions and industries from Korea and around the world. KAIST will support companies in taking the next steps after the event, helping turn discussions with researchers into technology transfer agreements, joint research and other forms of collaboration. It also plans to generate greater industrial and economic value from its research by tailoring technology marketing to industry needs and strengthening the links between technology transfer and startup creation. Companies interested in attending the briefing or requesting a consultation can register in advance through the KAIST TLO website. For inquiries about the event or participation, please contact the KAIST Office of Technology Value Creation. Registration: KAIST TLO website  https://tlo.kaist.ac.kr/ Contact: KAIST Office of Technology Value Creation Tel: +82-42-350-2978 Email: iptc@kaist.ac.kr

Moving beyond today’s AI, which excels at solving problems similar to those encountered during training, KAIST has launched a dedicated research institute to develop foundational technologies for Artificial General Intelligence (AGI) that can make decisions and adapt independently when faced with unfamiliar problems and environments. KAIST announced that it officially launched the AGI Research Institute under the KAIST AI Research Institute (AIRI) on September 18, following the completion of internal approval procedures. The research team initially comprises 11 professors: Kim Tae-Kyun (T-K), Kim Hyunwoo, and Oh Tae-Hyun (School of Computing); Sung Minhyuk (Department of AI Computing); Shin Jinwoo and Ye Jong Chul (Kim Jaechul Graduate School of AI); Kim Junmo and Chung Joon Son (Department of Electrical Engineering); Ahn Sungjin (Department of AI Computing); and Park Noseong and Yoon Sung-Eui (School of Computing). Professor Kim Tae-Kyun (T-K) of the School of Computing was appointed as the institute’s inaugural director, with plans to gradually expand the team. The AGI Research Institute has been established with the mission of advancing the science and engineering of AGI through fundamental research and returning the benefits to society. Its core research focus is “generalization”—the ability of AI to apply learned knowledge to previously unseen tasks, environments, and data domains. While current AI systems have achieved remarkable results through the scaling of data and computational power, they still struggle to learn and reason beyond their learnt domains. They perform well on problems similar to their training data, but their performance can decline when conditions or environments change. The institute plans to move beyond simply scaling data and computing resources by developing foundational technologies that enable AI to reason about previously unseen problems and adapt to new environments, even with limited resources. To this end, the institute will investigate the fundamental principles of AI through research into novel and efficient neural network architectures, learning principles, understanding the physical world, and the implementation of intelligence. It will also cultivate the next generation of research leaders. The institute’s key research areas include: • Efficient Foundation Models: Achieving high performance with less computation. • Novel Knowledge Discovery (AI Meta-Scientist): Enabling AI to generate its own hypotheses and discover new knowledge. • World Understanding: Developing world models that understand the structure and dynamics of the physical world and predict the future. • Physical AI: Enabling embodied systems, such as robots, to perceive and act within real-world environments. • AI Safety: Ensuring that AI remains aligned with human intent. In Efficient Foundation Models, the institute will develop neural network architectures and learning methods that go beyond Transformers, with the aim of enabling AGI even in resource-constrained environments. In AI Meta-Scientist research, it will develop AI systems that go beyond exploring existing knowledge to generate their own hypotheses, conduct scientific research, and discover new knowledge. By integrating these interconnected research areas into a single research institute, KAIST aims to establish itself as a global hub for AGI research. Director Kim Tae-Kyun said, “There are limits to how far AI can advance simply by continuing to expand AI data centers.” He added, “Sustained efforts to develop foundational AI technologies that can adapt to new problems even in resource-constrained environments will be critical to staying competitive in the next generation of AI.” The institute will pursue its research through national R&D projects supported by the Ministry of Science and ICT. Funding includes a total of KRW 10 billion over five years for the development of lightweight, low-power next-generation neural network architectures and KRW 18.75 billion over five years for AI Meta-Scientist research. The institute is also participating in a project led by LG Electronics to develop world models and robotics-based Physical AI technologies. That project has a total budget of KRW 34 billion over two years. In addition, the institute is conducting research on physically grounded intelligence. Choi Sung-Yool, Vice President of Research at KAIST, remarked, “I expect KAIST’s outstanding AI research teams to make significant contributions to developing core next-generation AGI technologies that lead the way toward sustainable human development.” Hong Jin-bae, President of IITP, stated, “As creative ideas and challenging research drive AI technological innovation, I look forward to the new AGI research institute becoming a hub of innovation that unlocks new possibilities for the future of artificial intelligence.”

KAIST announced that its Board of Trustees elected Oh Hyun Kwon, former Chairman and CEO of Samsung Electronics and Chairman of the Orange Planet startup foundation, as its new chair at the board’s 298th extraordinary meeting in Seoul on September 21, 2026. Prior to the election, Kichul Cha, a KAIST trustee and CEO of InBody, had served as acting chair since the term of former Chair Myung Ja Kim ended in late June. Kwon earned a bachelor’s degree in electrical engineering from Seoul National University in 1975, a master’s degree in the same field from KAIST in 1977, and a doctorate in electrical engineering from Stanford University in 1985. He joined Samsung in 1985 as a researcher at its Semiconductor Research Institute in the United States. In 1992, he led the development of the world’s first 64Mb DRAM, helping lay the foundation for Samsung’s strategy of building a decisive technological lead over its competitors. An engineer who rose through the ranks to become a leading business executive, Kwon served as President and Head of Device Solutions Division, Samsung Electronics from 2008 to 2011 and as Vice Chairman and CEO from 2012 to 2017. From 2017 to 2020, he held the positions of Chairman at both Samsung Electronics and the Samsung Advanced Institute of Technology. He is credited with helping propel South Korea's semiconductor industry to global leadership. After stepping back from corporate management, Kwon served as President of the Korea Invention Promotion Association from 2021 to 2022 and as the seventh Chair of Seoul National University’s Board of Trustees from 2023 to 2025. In September 2025, he became the first business executive to chair the Committee on Mid- to Long-Term Strategy Committee, now under the Ministry of Planning and Budget (then called the Ministry of Economy and Finance). He has also chaired Orange Planet since 2021, supporting young entrepreneurs. His honors include the Gold Tower Order of Industrial Service Merit in 2009, the Korea Best Scientist and Engineer Award in 2014, the Inchon Award in Industrial Technology category in 2012, and the Dasan Technology Award in 1997. He is also the author of Super Gap (2018) and Again, Super Gap (2020). Kwon has maintained close ties with his alma mater, KAIST. In 2018, for instance, he became the first KAIST alumnus to receive an honorary doctorate from the university, earning the distinction in business administration. He also helped establish the Kwon Oh Hyun Assistant Professor program, which provides annual support for promising faculty members pursuing creative and ambitious research in physics, mathematics, chemistry, biological sciences, and engineering. Kwon joined the KAIST Board of Trustees at its 297th extraordinary meeting in August before being elected chair at the September meeting. His term as chair runs from September 21, 2026, the date of the board resolution, through August 19, 2029, when his term as a trustee expires. Drawing on his experience in building Samsung’s technological leadership, Kwon is expected to help KAIST advance innovation in research and education and further strengthen its global competitiveness.(END)

AI will take the wheel in "Mario Kart," while students use generative AI tools to tackle an eight-hour hacking contest, as KAIST and POSTECH bring their scientific rivalry to the playing field. KAIST (President Choongsik Bae) and POSTECH (President Seong Keun Kim) announced on September 18 that the 23rd KAIST-POSTECH Science War, also known as the "KA-PO War," will take place at POSTECH on September 18 and 19. First held in 2002, the annual event brings students from both universities together for scientific competitions, sports matches, and performances. The two universities take turns hosting the event in Daejeon and Pohang. Following convention, the visiting university's name comes first in the event title, making this year's edition the "KA-PO War." The name "Science War" reflects the event's distinctive combination of technical challenges and athletic competitions. This year's games feature seven events. The four sports events are baseball, soccer, basketball, and esports matches featuring "League of Legends", while the three science events are a hacking competition, an AI competition, and a science quiz. This year's science events highlight current technology trends, incorporating generative AI and concepts from autonomous driving into the competitions. The hacking competition will run for eight hours, from 2:00 p.m. to 10:00 p.m. on the first day. The competition will present information security challenges, including cryptography problems, in rounds, with participants earning points for solving them within the allotted time. The use of generative AI tools is fully permitted. A screen will display live scores and participants' progress solving the challenges so spectators can follow the action. Also beginning at 2:00 p.m. on the first day, the AI competition will take place at the POSTECH eSports Colosseum. AI programs developed and trained by students from both universities will control karts in the popular racing game "Mario Kart Wii" and compete head-to-head. With the AI used to drive the karts without human input, the event serves as a virtual autonomous driving competition. Scores across four courses will determine the winner of each round. The first team to win two of three rounds will win the match. The science quiz will begin at 2:00 p.m. on the second day on the stage in front of POSPLEX, combining questions on general science and recent research trends with 'rock-paper-scissors chess,' a chess variant that tests strategic thinking and teamwork alongside scientific knowledge. The university that wins at least four of the seven events will be crowned this year's overall champion. KAIST currently leads the all-time standings with 13 overall victories to POSTECH's eight. "We hope this year's KA-PO War goes beyond a friendly exchange to become a special festival where students from Korea's two leading science and technology universities compete fiercely with their pride on the line, while sharing laughter and enjoying themselves together," said Dong-hyeok Lee, chair of the KA-PO War organizing committee and a student in KAIST's Department of Materials Science and Engineering who entered KAIST in 2024. "The KA-PO War is more than a competition to determine winners and losers. It is a special festival where KAIST and POSTECH students share their passion and talents through science and sports," said KAIST President Choongsik Bae. He added that he hopes the event inspires students to take pride in their role as future leaders of Korean science and technology and gives them a valuable opportunity to learn from one another and grow together through competition.

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