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KAIST Reads “Stress” from Smartphone Chatting, Searching, and Payment Habits
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.
2026.10.15 View 330 -
KAIST to Chart a Platform-FirstCourse for “Physical AI” Beyond Humanoids
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.
2026.10.12 View 799 -
KAIST to Showcase Noise Reduction and Robotics Technologies at 2026 Tech Fair
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
2026.10.12 View 483 -
KAIST Develops Vector Database Technology for Reliable Retrieval of Up-to-Date Information
When new information is continually added and old information is removed, an AI system may fail to find the correct material even when that material exists. KAIST researchers have developed a vector database technology that preserves the search paths leading to the needed information as data changes. This allows AI to find up-to-date information more accurately and quickly.
KAIST (President Choongsik Bae) announced on October 5 that a research team led by Professor Min-Soo Kim from the School of Computing has developed CONDA (Connectivity-Aware Dynamic Index). CONDA is a dynamic vector database indexing technology that maintains high search accuracy even as data is continuously added and deleted.
Generative AI models do not inherently have access to information that emerges after training. Retrieval-augmented generation (RAG) has been widely adopted to address this limitation. RAG searches sources such as recent news or internal company documents and uses the results in its answers.
For RAG to work properly, the system needs a search technology that can quickly and accurately find the information relevant to a question among vast amounts of data. Many AI services today convert the meaning of documents or images into vectors, which are numerical representations that computers can process. They then link pieces of information with similar meanings and follow these links to find the material they need.
The challenge is that data in real companies and on the internet changes constantly. New documents, news, and product information keep arriving, while older information is modified or deleted. As these changes repeat, the links built between data points can gradually break, a problem the researchers call "connectivity collapse." The needed information is still in the database, but the AI can no longer reach it, and search accuracy drops. In other words, the information itself has not been lost. The search path that leads to it has been broken.
CONDA considers both distances between data points and whether the search paths needed to reach the information remain connected. When new data is added or existing data is deleted, CONDA preserves the links that are important for search. This prevents specific pieces of information from becoming isolated in the search graph.
In experiments where data was constantly added and deleted, CONDA improved search accuracy (recall) by up to 24.5% compared with state-of-the-art methods. It also increased data update throughput by up to 1.90 times.
The team also ran searches and data updates at the same time for six hours on a large-scale dataset containing 100 million vectors. Throughout the test, CONDA recorded the lowest search latency and the highest search accuracy among the compared methods. This result suggests that the technology can be applied to large-scale AI services.
The technology can be used in knowledge search systems where information such as internal company regulations and work documents changes frequently. It can also be used in a range of AI services where data changes continuously, including news search, product search, and recommendation systems. In particular, it is expected to serve as a foundational technology for RAG, in which generative AI searches external information and uses it in its answers. CONDA allows this up-to-date information to be retrieved reliably over extended periods.
The research results are also being applied to a commercial product. CONDA is being integrated into AkasicDB, the database product of GraphAI, an AI data infrastructure company founded by Professor Kim. It is scheduled for commercial release in the fourth quarter of this year.
"The key value of retrieval-augmented generation (RAG) is that it allows large language models (LLMs) to find and use the latest information they were not trained on at the moment it is needed," said Professor Kim. He added that the study is significant because it presents a data infrastructure that lets AI use up-to-date knowledge accurately over long periods, even in real-world environments where data changes constantly.
Darae Lee, who completed a master's degree at the KAIST School of Computing, is the first author of the study, and Professor Kim is the corresponding author. The findings were presented on September 2 at VLDB 2026 (the International Conference on Very Large Data Bases), a leading international conference in the database field.
Paper title: CONDA: A Connectivity-Aware Dynamic Index for Approximate Nearest Neighbor Search over Evolving Data
DOI: 10.14778/3836663.3836694
This research was supported by the Mid-Career Researcher Program of the National Research Foundation of Korea (NRF) and the SW Star Lab program of the Institute of Information & Communications Technology Planning & Evaluation (IITP), both funded by the Ministry of Science and ICT (MSIT).
2026.10.06 View 888 -
KAIST Develops AI Chip That Recognizes Changes in Motion over Time
For a wearable device to distinguish walking from a brief swing of the arm, it must recognize how a movement changes over time. Korean researchers have developed a technology that stacks semiconductor devices with different response speeds, allowing them to recognize both a change that has just occurred and the changes before it. The technology could be applied to small devices that are worn on or attached to the body to analyze movement and physiological signals.
KAIST (President Choongsik Bae) announced on September 29 that a research team led by Professor Jimin Kwon from the Department of AI Systems, together with researchers from UNIST (President Chong Rae Park) and POSTECH (President Seong Keun Kim), has made a solid thin film from a material that contains ions and used it to stack semiconductor devices with different response speeds in multiple layers.
In these devices, applying a voltage moves the ions within the material and changes the flow of current. When the voltage is removed, the ions require time to return to their original state. As a result, the effect of previous signals remains in the device for a short time. This slow movement has generally been considered a disadvantage, but the research team used it to identify earlier inputs.
The main challenge was controlling how long this effect lasts. Until now, such materials have mostly been liquids or soft gels, which made it difficult to fabricate the devices precisely or to stack them in multiple layers.
To address this challenge, the team made the material into a solid thin film, known as a solid-state ionogel, and adjusted the amount of ions in the film as well as its thickness. This allowed them to create devices that respond to signals either quickly or slowly. When the two types were stacked, one layer responded to the most recent signal, while the other responded to signals that had accumulated over a longer period.
The researchers confirmed that the devices could distinguish all 16 patterns created when four consecutive input signals are turned on or off in different combinations. Using the responses measured from the devices, they also built a simulation model of a system that classifies videos of moving handwritten digits. The model achieved validation accuracies above 90% for sequences of moving images played at different speeds.
The team fabricated the devices on both 4-inch wafers and flexible substrates. Their electrical characteristics remained stable for 55 months after fabrication.
The results could be used to develop technologies for processing information that changes over time, such as movement and signals from the body, directly on small devices. Further validation is needed to determine how the technology performs, and how much power it saves, in actual smartwatches.
"These devices can be fabricated on large-area substrates using existing thin-film semiconductor processes, and they can also be stacked in multiple layers," said Professor Kwon. He added that the technology could be developed into AI chips that analyze movement and physiological signals for devices that must operate with low power consumption, such as smartwatches.
Research Professor Haksoon Jung from KAIST is the first author of the study, and Professor Yong-Young Noh of POSTECH and Professor Jimin Kwon of KAIST are the co-corresponding authors. The findings were published in Advanced Materials on June 30.
Paper title: Monolithic 3D-Integrated All-Solid Ion-Gated Carbon Nanotube Transistors With Tunable Ionic Conductance for Multi-Timescale Reservoir Computing,
DOI: 10.1002/adma.202523703
This research was supported by the National R&D Program through the National Research Foundation of Korea (NRF), funded by the Korean government (Ministry of Science and ICT, MSIT) (RS-2023-00281195 and RS-2024-00355384), and by the Korea Planning & Evaluation Institute of Industrial Technology (KEIT), funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) (RS-2024-00417909).
2026.09.29 View 1148 -
KAIST’s World-First Full-Course Robot Marathon Completion Published in Nature
A quadruped robot ran 42.195 km, a distance that is not easy even for humans to complete, without replacing its battery. KAIST researchers have developed this world-first achievement into design principles for long-distance operation, published the results in Nature, and are now moving toward commercializing robots that can be used in real industrial environments.
KAIST (President Choongsik Bae) announced on the September 24 that the long-distance running technology of RAIBO2, a quadruped robot developed by a research team led by Professor Jemin Hwangbo from the Department of Mechanical Engineering, and the results of its actual marathon completion were published in the international journal Nature.
This is the first time that robotics research conducted in Korea has been published in the main journal of Nature. Even globally, it is extremely rare for robotics research to be published in Nature, making this achievement an important demonstration of both the technological capability and academic level of Korean robotics research.
RAIBO2 completed the full 42.195 km course of the Sangju Dried Persimmon Marathon held in November 2024 in 4 hours, 19 minutes, and 52 seconds without charging or replacing its battery. It was the world’s first case of a quadruped robot completing the full course of an actual marathon event.
The core significance of this paper does not lie simply in the record of a robot running 42.195 km. In the global robotics industry, competition has recently intensified over the running speeds and obstacle-negotiation capabilities of quadruped robots and humanoids. However, to operate robots for long periods in real industrial environments, it is necessary to secure not only momentary locomotion performance but also energy efficiency, thermal management, battery performance, and actuator stability.
The research team approached long-distance operation not as a performance issue involving individual components such as batteries or motors, but as a “whole-robot system problem” in which mechanical structure, electrical systems, and locomotion control are closely interconnected.
Quadruped robots lose energy in multiple areas – including actuators, electrical systems, and mechanical components – as they repeatedly move their legs. Improving the efficiency of only one component makes it difficult to achieve both fast and stable locomotion performance and long travel distances at the same time.
The research team systematically analyzed energy losses throughout the entire robot and jointly designed the mechanical, electrical, actuation, and control systems so that energy consumption could be reduced while maintaining high locomotion performance. Through this approach, the researchers presented specific design principles for which factors must be considered together to enable quadruped robots to move stably for extended periods.
In particular, the research team went beyond short-distance laboratory running tests or maximum travel distances estimated through calculations and validated the technology on an actual full marathon course.
During the marathon, the team recorded the robot’s internal voltage, current, temperature, and battery status in real time and synchronized the data with position, speed, and altitude information measured by GPS. RAIBO2 was officially registered as a participant, allowing formal split times to be recorded, while the researchers ran alongside the robot and filmed the entire journey, lasting more than four hours from the starting line to the finish.
While the 42.195 km completion record demonstrated RAIBO2’s performance, the data accumulated throughout the process provided scientific evidence showing how that performance was made possible and how it was maintained in a real-world environment. The research team developed a single impressive demonstration into research results based on system design principles and actual measurement data that can be analyzed and revalidated.
The research team’s next challenge is to transfer the performance achieved with a single research robot into a product that can be used repeatedly in industrial environments.
RAIBO2 is a research robot that was designed and built by hand by the researchers themselves. To develop it into a product that can be deployed in real industrial environments, the same performance must be achieved reliably across multiple robots while also reducing production costs. The product must also meet industrial reliability requirements, including water and dust resistance, operation across a wide temperature range, durability against shock and vibration, and electromagnetic interference and compatibility requirements (EMI/EMC).
Raion Robotics, a KAIST faculty startup that originated from Professor Hwang’s laboratory, is responsible for connecting the research results to actual products. During the development of RAIBO2, Raion Robotics’ manufacturing technology also contributed to maintaining the high energy efficiency and locomotion performance achieved in the laboratory for more than four hours during the actual marathon.
The research team and Raion Robotics developed not only the mechanical and electrical systems, software, and artificial intelligence but also the motor drivers that precisely control the robot’s motors, allowing them to optimize the entire system.
Currently, Raion Robotics is using this systems-design capability to develop commercialization and mass-production technologies for quadruped robots that can operate for extended periods in real environments, going beyond research prototypes. The goal is to reproduce the world-class performance demonstrated by a single laboratory robot across multiple products and enable stable operation in a wide variety of environments.
Professor Jemin Hwang said, “This study goes beyond demonstrating how far a quadruped robot can run. Using actual marathon data, we demonstrated how the entire robot must be designed to achieve both high locomotion performance and energy efficiency,” adding, “We will connect the world-class performance achieved in the laboratory to products that anyone can use reliably in industrial environments.”
Dr. Choongin Lee and students Donghoon Youm and Jeongsoo Park of the KAIST Department of Mechanical Engineering participated in the study as co-first authors, and the research results were published in the international journal Nature on September 23 (local time).
Paper title: A quadruped robot designed to complete a marathon on a single battery charge, DOI: 10.1038/s41586-026-11102-5
Related photos and videos:
Comprehensive photo/video collection on Google Drive: https://drive.google.com/drive/folders/1ye82tTr2dCK2Bt7Ih0hSgWqkgqlVXk6X?usp=sharing
Full video of the marathon completion on YouTube: https://www.youtube.com/watch?v=-HMFoa3g9nA
The entire process of RAIBO2 completing the marathon can be viewed in the YouTube video.
2026.09.28 View 2348 -
KAIST Names Former Samsung Electronics Chairman and CEO Oh Hyun Kwon as Chair of Its Board of Trustees
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)
2026.09.22 View 3143 -
KAIST Develops Smartphone AI That Uses Past Solutions to Tackle Similar Problems
A new AI technology has been developed that retains and reuses knowledge, much like writing down a solution in a notebook and applying it to similar problems rather than asking an expert for help each time. Researchers at KAIST have developed a way for small AI models running on smartphones to store and reuse knowledge from a large server model. The approach reduced server calls by an average of 55.61% compared with a non-cumulative approach while maintaining high accuracy, suggesting that mobile AI could make faster decisions with less server support as it encounters similar problems.
KAIST (President Choongsik Bae) announced on September 21 that a research team led by Professor Jae-Gil Lee from the School of Computing has developed CURE (Cumulative Knowledge Reuse), a technology that enables a small AI model on a device to work efficiently with a large model on a server.
Smartphones have limited processing power and memory, so they typically use small, lightweight AI models. These models can handle simple tasks quickly but may be less accurate when identifying complex or unfamiliar images.
Sending every input to a powerful server model can improve accuracy, but transferring data and waiting for a response takes time. It also adds to network traffic and the server’s computational workload, increasing delays for users and operating costs for service providers.
Researchers have explored a collaborative approach in which the on-device model assesses each input first and sends only difficult cases to the server. Without a way to retain the server’s knowledge, however, the device uses each answer once and may need the same help when a similar input appears. This is much like a student who fails to write down the solution to a difficult problem and has to ask for help again when faced with another of the same kind. The team set out to turn these one-time answers into knowledge the device could continue to use.
CURE first checks whether the on-device model can handle an input reliably. If it cannot, the system consults knowledge previously obtained from the server and stored on the device. It contacts the server only if that knowledge is also insufficient. The process follows three steps, moving from solving a problem independently to consulting previous lessons and, when necessary, asking an expert.
For example, an on-device model that cannot identify a car model in a photo may turn to the server for help. CURE uses the server’s prediction and the image’s features to update the device’s knowledge store. When the device later receives a photo of a similar car model, it can use that knowledge to identify it locally.
CURE does not keep a collection of the original photos processed by the server. Instead, it stores a summary of their shared features and differences. This is like noting a car’s distinguishing features, such as its body shape or headlight design, rather than memorizing the entire photo. The stored knowledge can therefore help the system recognize not only images it has already seen but also similar images it encounters for the first time.
The team tested CURE using vision-language models, which connect images with text to understand visual information. These models link what they see to language, much as people do.
The researchers used MobileCLIP2 on the device and EVA-CLIP, which has 18 billion parameters, on the server. Parameters are numerical values within a model that encode information learned during training. They help the model distinguish objects and identify relationships between them.
In tests on a range of image classification datasets, CURE achieved accuracy close to that of an approach that sends every input to the large server model. It also made an average of 55.61 percent fewer server calls than a device-server collaboration baseline that does not retain knowledge from previous server responses.
In end-to-end tests that included communication time, CURE ran up to 2.80 times as fast as the non-cumulative device-server collaboration baseline and up to 3.67 times as fast as the approach that sends every input to the server. Making fewer server calls reduced the time spent transferring data and waiting for responses.
CURE requires no retraining of either the on-device model or the server model. It leaves both models unchanged and adds a separate store for knowledge obtained from the server, making it adaptable to a range of AI models and services.
The technology could reduce repeated data transfers and server computation image recognition. This could mean shorter waits for users and lower operating costs for service providers.
Potential applications also include robots and wearables that need to recognize their surroundings with limited computing resources. Robots operating over slow or unreliable networks, for example, could use previously acquired knowledge to make more decisions locally without waiting for a server response.
The benefits in practice will depend on the device’s processing power and storage capacity, network conditions, and the characteristics of the input data. Further testing across different devices and environments is therefore needed.
“CURE allows a small on-device AI model to remember and reuse knowledge it has already obtained, rather than repeatedly asking the server the same question,” said Professor Jae-Gil Lee. “By maintaining high accuracy while reducing communication demands and response times, we expect it to help smartphones, robots, and wearables use powerful AI models more efficiently.”
Dr. Youngjun Lee, a postdoctoral researcher from the KAIST Institute of Information Electronics, was the study’s first author, and Professor Jae-Gil Lee from the School of Computing was the corresponding author. Co-authors were Doyoung Kim from Amazon, Junhyeok Kang from LG AI Research, and Professor Hwanjun Song from the KAIST Department of Industrial and Systems Engineering. The findings were presented on September 10 at the European Conference on Computer Vision (ECCV 2026), a leading international computer vision conference held in Malmö, Sweden, from September 8 to 12.
Paper title “CURE: Cumulative Knowledge Reuse for Efficient Device-Server Hybrid Inference in Vision-Language Models”,
DOI: https://doi.org/10.1007/978-3-032-37627-5_21
Author information — Dr. Youngjun Lee (postdoctoral researcher, KAIST Institute of Information Electronics, first author), Doyoung Kim (KAIST graduate, now at Amazon, co-author), Junhyeok Kang (KAIST graduate, now at LG AI Research, co-author), Professor Hwanjun Song (KAIST Department of Industrial and Systems Engineering, co-author), Professor Jae-Gil Lee (KAIST School of Computing, corresponding author)
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2020-II200862, DB4DL: High-Usability and Performance In-Memory Distributed DBMS for Deep Learning, 50% and No. RS-2022-II220157, Robust, Fair, Extensible Data-Centric Continual Learning, 40%), the InnoCORE pro-gram of the Ministry of Science and ICT (AI Meta-Scientist, N10260110, 10%), and Samsung Electronics Co., Ltd.(IO251216-14634-01).
2026.09.21 View 2474 -
KAIST Develops Microbially Produced Adhesive Materials from Glucose Instead of Petroleum
KAIST researchers have demonstrated the potential of using microorganisms to produce a bio-based alternative to petroleum-derived materials for hot-melt adhesives. These heat-activated glues are widely used in packaging, furniture, electronics, and automobiles. Using Escherichia coli, the team produced a new polymer from glucose and demonstrated adhesive performance and thermal properties that support its potential use as a hot-melt adhesive.
KAIST (President Choongsik Bae) announced on September 17 that a research team led by Distinguished Professor Sang Yup Lee from the Department of Chemical and Biomolecular Engineering developed a microbial cell factory by engineering E. coli to produce the new aromatic polyhydroxyalkanoate (PHA) polymers from glucose for adhesive applications.
The research team applied systems metabolic engineering, an approach that redesigns a microorganism's overall metabolism to produce a target compound, to re-engineer the metabolic pathways of E. coli. Through this approach, the researchers successfully produced two new adhesive materials from glucose, poly(4HB-co-PhLA) and poly(3HB-co-4HB-co-PhLA), both belonging to the PHA family of biodegradable polymers.
Adhesives are essential materials used in a wide range of industries, including packaging materials, electronics, automobiles, and construction materials. In particular, hot-melt adhesives (HMAs) are solid adhesives that are melted with heat for bonding and then solidify as they cool. Because they do not require separate organic solvents and enable rapid bonding, they are widely used in industry.
However, most hot-melt adhesives currently in use are based on petroleum-derived polymers such as ethylene-vinyl acetate (EVA). Although they offer excellent adhesive performance, most do not readily degrade in nature and can cause waste and microplastic problems. In particular, even when biodegradable packaging materials are used, if the adhesive does not degrade, the biodegradability and recyclability of the entire product may be reduced.
To address this problem, the research team focused on PHA, a type of biodegradable plastic produced by microorganisms. PHA can be produced by microorganisms using renewable feedstocks such as glucose. It also has the advantage that properties such as polymer flexibility, strength, and heat resistance can be adjusted in various ways depending on which components are included and in what proportions.
Using this principle, the research team incorporated 4-hydroxybutyrate (4HB) and phenyllactate (PhLA) into a single polymer. 4HB contributes to the material’s softness and adhesion, while PhLA increases the material’s rigidity and heat resistance. When the research team varied the relative proportions of the two components, the thermal properties and adhesive performance of the polymers also changed. In particular, polymers containing approximately 24–34 mol% 4HB showed excellent adhesive performance.
The research team designed intracellular metabolic pathways so that E. coli consumes glucose, produces 4HB and PhLA on its own, and then links them together into polymers.
To address metabolic imbalances that arise when multiple monomers are produced simultaneously, the team adjusted the strength and timing of gene expression, added a CoA transferase to facilitate the reactions, and used a genome-scale metabolic model to identify and optimize metabolic pathways associated with insufficient precursor supply and production bottlenecks.
As a result, through fed-batch fermentation, in which nutrients are continuously supplied during microbial cultivation, the team successfully produced 10.2 g of poly(4HB-co-PhLA) per liter of culture medium. By additionally introducing a biosynthetic pathway for producing 3HB, the team successfully produced up to 52.8 g/L of poly(3HB-co-4HB-co-PhLA).
The research team also tested whether the produced polymers could be used as hot-melt adhesives.
In a lap shear adhesion test using stainless steel as the substrate, poly(4HB-co-PhLA) recorded a lap-shear strength of 4.58 MPa. This is higher than the 4.20 MPa achieved by a commercial EVA adhesive. Poly(3HB-co-4HB-co-PhLA) also showed performance comparable to EVA in a wood adhesion test.
In addition, poly(4HB-co-PhLA) retained a substantial level of adhesive strength even after repeated melting and rebonding. The incorporation of the PhLA component improved heat resistance.
Furthermore, after treatment with lipase, an enzyme that breaks down fats, surface damage to the polymers was observed, while the molecular weight and overall mass of the polymers also decreased. These results indicate that the newly developed aromatic PHA can be degraded by enzymes.
This study is significant not only for the development of an eco-friendly adhesive but also for demonstrating the potential of metabolic engineering to produce functional materials with tunable properties. Going forward, this approach could be extended into a biomanufacturing technology that tunes polymer properties, such as adhesion strength, flexibility, and heat resistance, to meet specific application needs, and produces such materials using microorganisms.
The approach also makes it possible to produce functional polymers that have traditionally been produced through petrochemical processes, from renewable feedstocks such as glucose instead. This is expected to serve as a sustainable production technology not only for adhesives but for a wide range of functional polymer materials going forward.
Distinguished Professor Sang Yup Lee said, “This study demonstrates that by engineering microbial metabolism, it is possible to go beyond simply producing polymers and directly produce functional materials,” adding, “In the future, by utilizing various non-natural monomers and microbial cell factories, this approach could be expanded into biomanufacturing technologies for the sustainable production not only of petroleum-based adhesive alternatives but also of a wide range of functional polymers.”
The study, co-authored by Ph.D. student Minju Kang, Dr. Youngjoon Lee, and Dr. Gi Bae Kim of the KAIST Department of Chemical and Biomolecular Engineering, was published online on July 20 in the international journal Nature Communications.
Paper title: Synthesis of poly(4HB-co-PhLA) and poly(3HB-co-4HB-co-PhLA) as sustainable hot-melt adhesives using engineered Escherichia coli
DOI: 10.1038/s41467-026-75809-9
Author information: Minju Kang (KAIST), Youngjoon Lee (KAIST), Gi Bae Kim (KAIST), Sang Yup Lee (KAIST, corresponding author)
This research was supported by the National Research Foundation of Korea through a grant funded by the Ministry of Science and ICT. (Grant No. 2022M3J5A1056117).
2026.09.17 View 4486 -
KAIST Robot That Follows Verbal Directions and Checks Its Own Work Takes First and Second Place in International Competitions
A robot that can follow a single verbal instruction, navigate an unfamiliar building, and verify that it has reached the correct destination has earned top honors in two international competitions.
KAIST (President Chungsik Bae) announced on September 11 that a team led by Professor Hyun Myung of the Urban Robotics Lab in the School of Electrical Engineering took first place in an international challenge held in conjunction with ECCV 2026, a leading computer vision conference, and second place in a challenge at RSS 2026, a leading robotics conference.
The team’s technology goes beyond conventional navigation. It enables robots to interpret their surroundings and independently verify whether they have made the right decision.
Both competitions focused on embodied AI—the ability of a robot to understand human language, perceive its surroundings through cameras, make decisions, and act on them. Unlike AI systems that operate solely in the digital realm, embodied AI interacts directly with the physical world.
Professor Myung’s research team joined forces with the Field Robotics Research Section of the Electronics and Telecommunications Research Institute (ETRI) to form the “URL-FRRS” team. The team placed first among 112 teams worldwide in the VLNVerse Challenge, a vision-language navigation competition held at the ECCV 2026 EMR Workshop in Malmö, Sweden on September 9.
The competition tested whether a robot could reach the correct destination based solely on a stated goal, such as “Find the blue sofa in the living room,” or a multistep instruction, such as “Turn right at the end of the hallway and enter the second door.”
When the research team analyzed why existing robots were failing, they found that, surprisingly, the more common cause was not an inability to find the route, but the robot mistakenly concluding it had “arrived” at the wrong location. A robot might, for example, pass the actual destination or mistake a similar-looking room for the target.
To address this problem, the team developed CoRe-VLN, short for Coverage-based Recovery for Vision-Language Navigation. The technology prompts the robot to verify its location whenever it is about to stop, essentially asking itself, “Is this really the destination?”
Once the robot determines that it may have reached its destination, it scans its surroundings again. An AI model capable of interpreting both text and images then checks whether the robot is in the correct room, whether the target object matches the specified color and shape, and whether the object is actually nearby. The process is similar to a person checking a sign or looking around after arriving at a meeting point to make sure they are in the right place.
If the robot determines that it has stopped at the wrong location, it plans a new route and resumes its search. Because CoRe-VLN can be added to an existing navigation system without further training, it gives the robot a self-verification and recovery capability that can be readily applied in unfamiliar environments.
The team achieved an average success rate of 90.7% across the two competition tasks. Although it tied with the runner-up in success rate, it secured first place by submitting its results earlier. The third-place team recorded a success rate of 89.7%.
The KAIST team also took second place in the NaviTrace Challenge at the RSS 2026 OWN Workshop, held in Sydney, Australia, in July.
In this competition, a robot was given only a single photograph and a brief command, such as “Drive into the parking lot,” and had to determine the correct direction of travel.
When entering a parking lot, people instinctively distinguish the entrance from the exit by interpreting road markings and observing the direction of other vehicles. These cues, however, are not explicitly stated in the command. To navigate spaces shared with people, robots must be able to infer such implicit rules directly from the scene before them.
The team addressed this challenge by developing PRISM-Nav, a system in which multiple AI agents divide the work instead of relying on a single AI model to make every decision.
One agent locates the destination, another identifies hazards such as stairs and curbs, and a third recognizes socially significant features such as sidewalks and crosswalks. A final agent integrates their findings and determines the direction of travel. The approach resembles a group where one person checks the route, another watches for hazards, and a third interprets traffic rules before they jointly choose the safest path.
The researchers also streamlined communication among the AI agents. Instead of describing locations through lengthy text or numerical coordinates, the agents mark destinations and hazardous areas directly on an image and share the annotated image with one another, much like handing someone a marked-up map rather than explaining the route verbally. PRISM-Nav can also be deployed in new environments without retraining the AI models.
The team scored 53 points in the competition, finishing second behind a joint team from Nanjing University and FiveAges.
Notably, the team used multiple instances of the relatively inexpensive and lightweight Gemini 3 Flash model to outperform Gemini 3.1 Pro Preview, the more powerful model used by the organizers as a benchmark. The result demonstrated that coordinating several lightweight AI models can deliver better performance and cost efficiency than relying on a single, more expensive model to handle every task.
“We are delighted that the potential of our technology has been recognized in a competition with research teams from around the world. This achievement demonstrates that AI can make decisions and act independently in unfamiliar environments without additional training.” said Professor Myung.
He added, “We plan to extend this approach beyond navigation to tasks such as identifying, picking up, and moving objects, ultimately developing robots that can verify and correct their own decisions.”
As the technology advances, robots may move beyond simply executing human commands. Before acting, they could independently ask, “Is this the right place?”, “Is this the right object?”, or “Is this action safe?” Such capabilities could benefit not only delivery and guide robots but also robots operating alongside people in logistics centers, factories, hospitals, and other shared environments.
Professor Myung’s laboratory also took first place in international challenges held at ICRA 2026, a leading robotics conference, and CVPR 2026, a leading computer vision conference, in June. The laboratory is now expanding its expertise in spatial cognition into the field of physical AI.
This research was supported by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation (IITP). (END)
2026.09.16 View 3182 -
Single Material Enables Efficient Charge Injection into Two Types of Ultrathin Semiconductors, Paving the Way for Smaller, More Energy-Efficient AI Chips
A new approach could help make future AI chips smaller and more energy-efficient. A KAIST-led research team has used a single material to address one of the major obstacles facing atomically thin semiconductors, the difficulty of efficiently injecting charge. The technology could contribute to next-generation AI and low-power semiconductor devices in which multiple ultrathin layers are vertically integrated to increase device density and performance.
KAIST (President Choongsik Bae) announced on September 16 that a research team led by Professor Joonki Suh from the Department of Chemical and Biomolecular Engineering has developed a ‘universal van der Waals tunneling injector’ based on tin diselenide (SnSe2). The single-material injector efficiently supplies charge to two different types of atomically thin semiconductor channels.
The study was conducted in collaboration with researchers from Yonsei University, the Beijing Computational Science Research Center in China, the Korea Institute of Science and Technology (KIST), Hanyang University, the Ulsan National Institute of Science and Technology (UNIST), and Samsung Electronics.
A transistor is a microscopic switch that controls the flow of electric current. Nearly every semiconductor chip, from those used in smartphones and computers to advanced AI processors, contains vast numbers of transistors.
Transistors can be broadly classified as n-type or p-type. In n-type transistors, ‘electrons’ carry the current, while in p-type transistors, current is carried by ‘holes’, which are empty electron states that act as positive charge carriers. Modern semiconductor chips combine these two types in complementary metal-oxide-semiconductor, or CMOS, circuits, which enable fast operation while minimizing power consumption.
Two-dimensional semiconductors, which can be less than a nanometer thick, have recently emerged as promising materials for making chips smaller and more energy-efficient. Their atomically thin structure also makes it possible to stack multiple device layers vertically, potentially allowing more transistors to be integrated within the same footprint.
A persistent challenge, however, has been injecting charge efficiently into such ultrathin semiconductors. Depositing a conventional metal electrode directly onto a two-dimensional semiconductor can damage its delicate atomic structure during fabrication. The resulting interface can also create an energy barrier that obstructs the injection of electrons or holes.
In atomically thin semiconductors, the ‘entry point’ for electrical charge has been a major bottleneck limiting transistor performance. Complicating matters further, n-type and p-type channels generally require different contact conditions and have therefore relied on separately optimized electrodes.
The research team addressed this problem using a single material: tin diselenide (SnSe2), a layered semiconductor composed of tin (Sn) and selenium (Se). Instead of forming strong chemical bonds with the semiconductor channel, SnSe2 can be placed in contact with it through weak interatomic attractions known as van der Waals forces. This approach preserves the atomically thin channel and creates a clean, uniform interface.
Crucially, SnSe2 provides a different but favorable charge-injection pathway depending on the semiconductor with which it is paired.
When combined with p-type tungsten diselenide (WSe2), SnSe2 forms a type-III, or ‘broken-gap’, band alignment that enables efficient band-to-band tunneling. In this process, charge carriers pass through an energy barrier quantum mechanically rather than having to acquire enough energy to travel over it. When combined with n-type molybdenum disulfide (MoS2), the same SnSe2 injector forms a different junction. An applied gate field reshapes and narrows the electron-injection barrier, allowing electrons to tunnel through it.
In other words, whereas n-type and p-type transistors previously required separately optimized charge-injection contacts, SnSe2 serves as a single, shared injector that adapts to both channel polarities through different tunneling mechanisms.
The resulting performance improvements were substantial. In p-type WSe2 transistors, the SnSe2 injector increased the maximum drive current by more than 1,000 times compared with conventional nickel electrodes. Drive current refers to the maximum current that a transistor can deliver when switched on.
In n-type MoS₂ transistors, the SnSe2 injector enabled steep switching and an on/off current ratio exceeding one billion (109). This means that the device conducts current effectively in its on state while strongly suppressing current in its off state.
The team also fabricated a CMOS inverter, a basic building block of digital circuits that combines n-type and p-type transistors, and confirmed that it operated reliably in response to repeated input signals.
The key contribution of this research is that it demonstrated a single material can efficiently supply charge to both types of ultrathin semiconductors, moving beyond the conventional approach of using separate electrodes for n-type and p-type devices.
With further advances in direct growth, large-area fabrication, and device integration, two-dimensional semiconductors could eventually be stacked in multiple layers to form three-dimensional chips. Such architectures could accommodate more transistors within the same footprint and perform more functions while consuming less power, contributing to the development of next-generation AI processors and ultra-low-power electronic devices.
“This study demonstrates that efficient charge injection, one of the most challenging bottlenecks in monolayer two-dimensional semiconductors, can be addressed using a single material platform,” said Professor Joonki Suh. “With further advances in direct growth and large-area processing technologies, this approach could accelerate the practical implementation of low-power two-dimensional CMOS integrated circuits.”
The study was led by KAIST PhD candidate Hanbin Cho as first author, with Professor Kyungmin Ko from Yonsei University and Professor Joonki Suh from KAIST serving as co-corresponding authors. The paper was published online on August 12 in the international journal Advanced Materials.
Paper title: “A Universal van der Waals Tunneling Injector for Monolayer CMOS”
DOI: 10.1002/adma.74597Authors: Hanbin Cho of KAIST, first author; Professor Kyungmin Ko of Yonsei University and Professor Joonki Suh of KAIST, co-corresponding authors
This work was primarily supported by Samsung Electronics Co., Ltd. It also received support from Korean funding agencies, including the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT and the Ministry of Education.
2026.09.16 View 2684 -
KAIST Researchers Uncover How Neurons Deliver RNA to Where It Is Needed
Much like a courier reading an address on a package, neurons must deliver RNA to the precise locations where it is needed. A KAIST research team has discovered how a small chemical modification on RNA acts as a “delivery tag,” helping selected RNAs travel to distant regions of neurons.
The findings provide a basis for investigating RNA “delivery errors” in brain disorders—cases in which RNA is produced normally but fails to reach its intended destination—and may ultimately inform the development of new therapeutic strategies.
KAIST (President Choongsik Bae) announced on September 14 that a research team led by Professor Ki-Jun Yoon of the Department of Biological Sciences has identified a mechanism through which N6-methyladenosine, or m6A, facilitates the transport of specific RNAs to distal axons, the long, cable-like projections of neurons.
Unlike most other cells, neurons have long, highly branched extensions. Axons transmit signals to other neurons, while dendrites receive incoming signals. For the brain to develop and function normally, these structures must grow properly and form appropriate connections.
Because neurons can extend over considerable distances, RNA produced in the cell body may be needed far away, including at the growing tip of an axon. Neurons must therefore identify particular RNAs among thousands of candidates and transport them to the correct subcellular locations.
Just as producing a package does not complete its delivery—the package must also reach the correct address—producing RNA is only part of the process. RNA must arrive at the right place at the right time. However, the mechanisms through which neurons select and transport RNAs over long distances have remained incompletely understood.
The research team found that m6A, a small chemical modification added to RNA, plays an important role in this process. m6A is a modification of adenosine, one of the building blocks of RNA, and is known to influence several aspects of RNA metabolism, including stability and translation. The new study shows that m6A can also function as a molecular “delivery tag” that promotes the transport of specific RNAs to distal regions of neurons.
The researchers first examined mice in which Mettl14, a component of the enzyme complex that installs m6A on RNA, was deleted from developing neurons. In the absence of normal m6A modification, the neurons exhibited impaired axon projection and neurite development. These findings confirmed that m6A plays an important role in the early development of neurons.
The team then used a high-resolution technique known as m6A-SAC-seq to map the precise locations of m6A modifications in RNA from the developing mouse brain at single-nucleotide resolution. The resulting map revealed that RNAs associated with axon development and synapse organization were among those marked by m6A.
The researchers also identified how this “delivery tag” is recognized and connected to the cellular transport machinery. They found that YTHDF2, a protein that recognizes m6A-modified RNA, works with the RNA-binding protein FMRP and the motor protein KIF5C to facilitate the transport of RNA along neuronal processes.
In the delivery analogy, m6A serves as the tag attached to the RNA, YTHDF2 reads the tag, and FMRP and KIF5C help connect the RNA to the cellular machinery that carries it toward its destination.
YTHDF2 has previously been studied primarily for its role in promoting the degradation of RNA that is no longer needed. This study reveals an additional function: in developing neurons, YTHDF2 also helps transport specific RNAs to distal locations rather than simply directing them toward degradation.
The findings offer a new perspective on the study of brain disorders. Research has traditionally focused on whether RNA is produced and maintained correctly. The new results suggest that researchers should also consider whether normally produced RNA is delivered to the correct location within a neuron.
In other words, neurological dysfunction may potentially arise not only from defects in RNA itself, but also from problems with the molecular tags or transport machinery that determine where the RNA travels.
Future studies could investigate whether such RNA delivery errors occur in neurodevelopmental or neurodegenerative disorders and whether they contribute to neuronal dysfunction or disease onset. If such links are established, the findings could eventually support the development of therapeutic approaches that regulate RNA transport and restore the delivery of specific RNAs to their intended locations.
The high-resolution m6A map of the developing nervous system generated in this study may also serve as a valuable resource for examining how RNA modification and intracellular transport change during brain aging and neurodegenerative disease.
“Our study shows that a small chemical modification on RNA does more than regulate RNA stability—it also plays an important role in delivering specific RNAs to the locations where they are needed within neurons,” said Professor Yoon. “We hope these findings will provide a foundation for understanding why RNA may fail to reach its intended destination in brain disorders and for exploring new therapeutic possibilities.”
Bonsang Koo, Dr. Ajeet Kumar, and Huiseon Hwang of the KAIST Department of Biological Sciences contributed equally to the study as co-first authors. The paper was published in Nature Communications on July 30.
Paper title: The m⁶A reader YTHDF2 regulates mRNA transport and axon outgrowth in developing neurons
DOI: 10.1038/s41467-026-76161-8
Author information: Co-first authors (Bonsang Koo, Ajeet Kumar, Huiseon Hwang); Contributing authors (Minyeong Park, Sung-Min Lee, Ki-Heon Lee, Ji-Hoon Moon, Jaewon Yang, Chan-Woo Park, Saebom Lee, Hee-Jung Jo, Woo-il Kim, Eunji Cho, Hagyeong Lee, Chang-Won Lee, Jae-Sung Woo, Jong Hyuk Yoon, Yoon Ki Kim); Corresponding author (Ki-Jun Yoon)
This work was supported by National Research Foundation of Korea (NRF) grants (2020M3A9E403967022, RS-2024-00332454, RS-2024-00440778, and RS-2026-25488918 to K.-J.Y.) funded by the Korean Ministry of Science and ICT, and Future Planning (MSIP), the POSCOScience Fellowship from the POSCO TJ Park Foundation (to K.-J.Y.), the Young Investigator Grant from the Suh Kyungbae Foundation (to K.-J.Y.), the Korea Brain Research Institute (KBRI) Basic Research Program through the Korea Brain Research Institute, funded by the Ministry of Science and ICT (MSIT) (24-BR-02-03 to J.H.Y).
2026.09.14 View 3983