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KAIST Reveals How Information Is Written in Ferroelectric Memory at the Nanoscale
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.
2026.10.08 View 75 -
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 317 -
KAIST Identifies a “Hidden Amplification Switch” in Lymphangiogenic Signaling
Lymphatic vessels act as the body’s “drainage system,” collecting excess fluid from tissues. When lymphatic drainage is impaired because lymphatic vessels are underdeveloped, damaged, or dysfunctional, lymphedema can develop, causing swelling, particularly in the arms and legs. Conversely, excessive lymphatic vessel growth around tumors can facilitate cancer metastasis by providing routes for tumor cells to spread. Korean researchers have discovered that VEGF-C brings two VEGFR-3 receptors together, and the resulting VEGF-C–VEGFR-3 complexes further cluster to amplify signaling. Targeting this clustering process may provide a new strategy for either enhancing or suppressing lymphangiogenic signaling, depending on the disease context.
KAIST (President Choongsik Bae) announced on October 2 that a joint research team led by Professor Ho Min Kim from the Department of Biological Sciences at KAIST and Dr. Sangkyu Lee of the Institute for Basic Science (IBS; President Suk Bok Chang) determined the three-dimensional structure of the VEGF-C–VEGFR-3 complex and identified how higher-order clustering of these complexes amplifies signaling.
When VEGF-C binds to VEGFR-3 on the cell surface, the receptor is activated and signals that regulate the formation and function of lymphatic vessels are transmitted into the cell. Until now, VEGF-C-mediated dimerization of two VEGFR-3 receptors has been considered a key step in receptor activation. However, how the signal becomes further amplified after this step has not been fully understood.
The research team examined the structure of VEGF-C bound to VEGFR-3 using cryogenic electron microscopy (cryo-EM), which enables detailed visualization of the three-dimensional structures of proteins. They found that VEGF-C-induced complexes containing two VEGFR-3 receptors further assembled side by side into higher-order clusters. This revealed a new structural mode by which VEGF-C–VEGFR-3 complexes can organize laterally along the cell membrane.Simply put, it is like two people first forming a team to begin a task, and then multiple teams gathering together to increase their collective strength. The researchers also tested whether this clustering actually amplifies signaling. By altering the regions where the complexes contact one another and by using light to control receptor clustering, they confirmed that the gathering of multiple complexes plays an important role in signal amplification.
This study identifies a new point at which lymphangiogenic signaling could potentially be regulated. In the future, researchers may explore ways to enhance signaling when lymphatic vessel formation is insufficient and to suppress signaling when excessive lymphatic vessels form around tumors. However, this study did not demonstrate therapeutic effects for lymphedema or the inhibition of cancer metastasis, and further research will be required before the findings can be applied to actual treatments.
Professor Ho Min Kim said, “This study reveals how two VEGFR-3 receptors first form a ligand-induced pair, after which multiple such complexes cluster together to further amplify the signal for lymphangiogenesis ,” adding, “By identifying this previously unseen ‘hidden amplification switch’ in lymphangiogenic signaling, we expect this work to provide an important foundation for developing new therapeutic strategies for related diseases such as lymphedema and cancer metastasis.”
Dr. Ryeongeun Cho of the KAIST InnoCORE AI-CRED Innovative Drug Research Group and Dr. Jinsook Ahn of the KAIST Department of Biological Sciences participated in the study as co-first authors. Professor Ho Min Kim of KAIST and Dr. Sangkyu Lee of IBS led the research as co-corresponding authors. The findings were published online on September 9 in the international journal Advanced Science.
Paper title: Structural Basis of Lymphangiogenic Receptor VEGFR-3 Activation Mediated by Distinctive Clustering of the Ligand–Receptor Complex, DOI: 10.1002/advs.77728
Related videos:
Video showing cryo-EM sample preparation by rapidly freezing protein samples at cryogenic temperatures: https://drive.google.com/file/d/1OGPpkzmZMaWU3oxfswpbcEtt3NzX3n4q/view?usp=drive_link
Video showing the grid screening process for cryo-EM image collection: https://drive.google.com/file/d/1Bg6zEPeS9NZDO8zFaj59Fe7L_f8tBWwQ/view?usp=drive_link
Video showing the loading of frozen protein samples into the cryo-EM instrument: https://drive.google.com/file/d/19c1INwek38B72KKPPXmPVTggYAoO4aY3/view?usp=drive_link
Titan Krios cryo-EM instrument installed at KAIST KARA: https://drive.google.com/file/d/1c5Wmj44_9taYIW4N_mumg6tT21iOUYNQ/view?usp=drive_link
Video of the molecular structure of the VEGF-C and VEGFR-3 complex identified in this study: https://drive.google.com/file/d/198bcFoTb_pv__Ep-MHwcI47G4gv5yisx/view?usp=drive_link
This work was supported by the National Research Foundation of Korea (RS-2024-00397681, RS-2025-00523615 to H.M.K. and RS-2025-00523575 to J.A.), the InnoCORE program of the Ministry of Science and ICT (N10250153 to H.M.K. and R.C.), the NEXUS & CELINE consortium, the KAIST Convergence Research Institute Operation Program, and the Institute for Basic Science (IBS-R001-Y4 to S.L.), among others.
2026.10.02 View 620 -
KAIST Develops a Venus Flytrap-Inspired 'Robot Hand' Material That Detects What Comes Near and Grasps It Under UV Light
A Venus flytrap closes its leaves to trap prey when it senses a stimulus. Taking inspiration from this plant, KAIST researchers have combined the roles of a skin that senses approaching objects and a muscle that grasps them in a single soft material. When an electrically charged object comes near, ions inside the material move and produce an electrical signal. When ultraviolet (UV) light is applied, the material bends and grasps the object. Because it holds its deformed shape for more than 10 minutes after the light is turned off, the material shows potential for soft robotic hands that require fewer components and less wiring.
KAIST (President Choongsik Bae) announced on September 30 that a research team led by Professor Hong Chul Moon from the Department of Chemical and Biomolecular Engineering has developed the ‘Ionograsper,’ an ionic soft robot that senses nearby objects and moves in response to light. An ionic material contains ions, small particles that carry a positive or negative electric charge, which allow the material to respond to electrical changes in its surroundings.
Humans notice objects with their eyes or skin and grasp them by moving their muscles. Robots likewise need sensors to detect their surroundings and actuators to generate movement. However, when these devices are attached separately to a soft robot, which bends flexibly, the number of components and the amount of wiring increase, and the structure becomes more complex.
The research team combined the roles of a robot’s skin and muscle in a single soft polymer material. Polymers are substances made of small molecules linked into long chains; plastics and rubber are common examples. The team combined azobenzene, a compound that changes shape when exposed to light, with a hygroscopic polymer that absorbs moisture from the air. This produced a network structure in which ions can move.
When a charged object approaches, its electric field causes the positive and negative ions inside the material to redistribute. As a result, changes occur inside the material even without direct contact. The researchers used the resulting electrical signal to detect the approach and movement of objects without applying a separate sensing voltage.
To make the material move, the researchers irradiate it with UV light. Azobenzene changes its molecular shape when it absorbs light, and moisture escapes from the irradiated side of the material. This causes the material to bend toward the light, which can be used to grasp an object.
When the light is turned off, moisture re-enters the material through nanoscale pores that formed on its surface during irradiation. The material then bends in the opposite direction and holds its deformed shape for more than 10 minutes, until the polymer structure slowly relaxes. This means the light does not need to stay on for the material to maintain its grasping position.
A defining feature of the study is that three functions, sensing an object, moving, and retaining a changed shape for a period of time, are realized in a single material. The researchers also analyzed how light-induced changes in the molecules and in moisture content lead to bidirectional bending and shape retention.
This material design approach could help reduce the components and wiring required when sensors and actuators are mounted separately on soft robots. The work, however, remains at the research stage: the material currently detects charged objects and is actuated by UV light. The team plans to increase the sensing distance, actuation speed, durability under repeated use, and load-bearing capacity, and to make the material responsive to visible or near-infrared light.
“Rather than attaching a robot’s skin and muscles separately, we created a single material that senses objects and moves when it receives light, and a key feature is that it maintains its deformed shape even after the light is turned off,” said Professor Hong Chul Moon. He added that the team plans to combine the material with AI control technologies and develop it into a sensing and actuation material for physical AI robots.
Professor Yong Min Kim of the School of Chemical Engineering at Jeonbuk National University served as the first author, and Professor Hong Chul Moon of KAIST’s Department of Chemical and Biomolecular Engineering served as the corresponding author. The study was published online in the international journal Advanced Materials on September 14, 2026, and was selected as the front cover article of Issue 62.
Paper title: A Multifunctional Ionograsper Enabling Ion-Redistribution Proximity Sensing and Structural-Reconfiguration-Driven Bidirectional Actuation,
DOI: 10.1002/adma.74981
Authors: Yong Min Kim (Jeonbuk National University, first author); Jin Han Kwon, Hyeon Woo Yang, Sungryong Kim, and Gyeong Rok Lee (KAIST, co-authors); Hong Chul Moon (KAIST, corresponding author)
Related Video: https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.74981
This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT, MSIT) (RS-2025-00554260) and by the Nano & Material Technology Development Program through the NRF, funded by the MSIT (RS-2025-02221332).
2026.09.30 View 1095 -
KAIST Develops a Genetic Switch That Works in Both Bacteria and Yeast
Microbial cell factories that produce pharmaceutical ingredients and other useful compounds include both the bacterium Escherichia coli and yeast, the organism used to leaven bread and ferment alcohol. The two organisms turn genes on by different rules, so a production design built for one has been hard to transfer to the other. A KAIST research team has now built a gene control element that works in both and tested it using a green pigment whose production requires several genes working together. The work is expected to help researchers select the microbe best suited to producing a target compound.
KAIST (President Choongsik Bae) announced on September 28 that a research team led by Professor Juyoung Lee from the Graduate School of Engineering Biology has developed a "hybrid promoter," a gene control element that works in both E. coli and yeast, two very different types of microorganism.
For a microbe to make a target compound, the genes responsible for producing it must be turned on. A promoter is the stretch of DNA located in front of a gene that controls how strongly that gene works.
The challenge is that E. coli and yeast turn genes on in different ways, and a promoter that performs well in E. coli may not work at all in yeast. Making the same compound in a different microbe has therefore required choosing a promoter suited to that host and retuning the output of each gene from the beginning.
The team's hybrid promoter combines, within a single sequence, DNA elements that E. coli and yeast each recognize. It is, in effect, a shared control element designed so that each organism can find the signals it needs even though the two read genes differently. By varying the combination of these elements, the researchers built a series of promoters spanning a range of strength from weak to strong.
The promoters worked in both hosts, and combinations that were strong in E. coli were generally strong in yeast as well. That does not mean the two microbes make equal amounts of a compound; it means the relative ranking of promoter strengths from weak to strong is preserved across hosts.
The team then tested whether the new control element could do more than turn on a single gene and could also be used for actual compound production. For this, they chose a green pigment whose production requires three genes acting in sequence. Under the strong control element, pigment output was about 3.1 times higher in E. coli and about 2.6 times higher in yeast than under the weak one. The result shows that the system can control the amount of a compound produced in both microbes. The team also confirmed that the promoters worked in other types of bacteria and yeast.
The approach could help researchers identify which microbe is best suited to producing a given compound, since the same gene control design can be applied across several microbes and the production results can be compared. It may reduce the need to redesign control elements from scratch whenever the host changes, although improving actual output will still require host-specific tuning.
"What matters about this work is that it gives us a common design principle, one that lets us predict a promoter's relative strength even when the microbe changes," said Professor Juyoung Lee. She added that she expects the approach to speed up biomanufacturing by making it faster to design and compare a range of microbial cell factories.
So-Hee Son, Soo Young Moon, and Nan-Yeong An of KAIST are co-first authors on the paper, which was published in Nucleic Acids Research on September 23 and first appeared online on September 8.
Paper title: Modular synthetic cross-kingdom promoters enable coordinated expression in Escherichia coli and Saccharomyces cerevisiae,
DOI: https://doi.org/10.1093/nar/gkag868
This work was supported by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF) through the Bio & Medical Technology Development Program (RS-2024-00445145) and the Basic Science Research Program (RS-2025-00518119), with additional support from the Korea-US Collaborative Research Fund (KUCRF), funded by MSIT and the Ministry of Health & Welfare (RS-2024-00468410).
2026.09.28 View 1297 -
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 1538 -
KAIST Finds a Way to Redirect Cell Fate with Just a Single Stimulus
Which genes need to be controlled to guide a stem cell toward a desired cell type, or to modulate an immune cell's inflammatory response? A Korean research team has developed a general-purpose computational framework that identifies the key targets needed to steer a cell toward a desired state with a single temporary intervention, without permanently altering its genes.
KAIST (President Choongsik Bae) announced on September 23 that a research team led by Professor Kwang-Hyun Cho from the Department of Bio and Brain Engineering has developed NUDGE, a foundational technology that redirects cell fate in a desired direction through a minimal, temporary intervention by harnessing the gene regulatory dynamics already present within cells.
Technologies that redirect cell fate have drawn attention as key tools for treating intractable diseases and advancing regenerative medicine, since they can be used to differentiate stem cells into cells with specific functions or to return diseased or aged cells to a near-normal state. Differentiation, for example, refers to the process by which a stem cell becomes a specific cell type with a distinct function, such as a cardiomyocyte or a neuron.
However, existing methods for controlling cell fate have relied mainly on permanent intervention like keeping specific genes locked in an "on" or "off" state. While this approach can produce the desired cell state, it has a limitation. It can reduce plasticity, the flexibility that allows cells to adapt to future changes in their environment, and it can also give rise to abnormal cell states that do not exist in nature.
Rather than forcing a cell into a fixed state, the research team devised a new strategy that lets the cell reach the desired state on its own through a single temporary stimulus. The approach nudges the gene regulatory dynamics already present inside the cell toward the target direction, much like briefly clearing a blocked channel and letting the water find its own course.
NUDGE (natural dynamics control of gene regulatory networks), the technology the team developed, uses a computational logic model to examine how key genes inside a cell influence one another and to determine which state the cell ultimately reaches as a result. It then mathematically decomposes the logical functions that describe these gene interactions to identify the minimal combination of control factors needed to convert a cell from its current state to the desired one.
For example, if the goal is to differentiate a stem cell into a cardiomyocyte, NUDGE functions as a "blueprint for minimal intervention" that identifies which molecular targets need to be temporarily activated or inhibited.
A key strength of the framework is that its exact method is mathematically guaranteed to identify all minimal control combinations that lead to the desired phenotype within the model. The team also presented an efficient approximation method that extends the framework to large-scale molecular networks.
NUDGE does more than identify which factors to stimulate. It can also analyze which pathways a stimulated cell follows to reach the target state and how stably that state is maintained once reached, making it possible to anticipate whether unexpected states might emerge during the transition.
The researchers assessed permanent control in 63 published biological network models where fixing a single node was sufficient to achieve the target phenotype; 55 showed reduced plasticity or attractors absent from the uncontrolled network. Separately, NUDGE's approximation achieved an average intervention error below 0.01 in more than 90 percent of 552 control problems across 69 large biological networks. Error measured the fraction of simulated converged states with the undesired phenotype. Of the minimum-sized, error-free interventions pooled from the three methods for each problem, NUDGE found an average of 83 percent, compared with 59 percent for IBMFA and 53 percent for LDOI.
To assess how well NUDGE explains real biological phenomena, the researchers applied it to three distinct processes: cardiomyocyte differentiation, fate determination in mast cells involved in allergic responses, and the conversion of macrophages, a type of immune cell, into an anti-inflammatory state.
In cardiomyocyte differentiation, the framework reproduced the known pattern in which MESP1, a gene important for heart development and regeneration, is briefly active early in differentiation but inactive in mature cardiomyocytes. The model shows how a single temporary intervention involving MESP1 and another regulatory target can guide differentiation without keeping MESP1 continuously active.
The team also identified GATA2 and GATA1 as key regulators of cell fate during mast cell formation. In the macrophage model, the team derived optimal strategies for a single temporary intervention, including one using IL-4, to induce anti-inflammatory M2 states while preserving the capacity to transition between inflammatory and anti-inflammatory phenotypes and among M2 subtypes.
The significance of this technology lies in providing a general-purpose framework that identifies where a single temporary intervention can guide a cell to a desired state, provided that a stable state with the desired phenotype exists in the network's uncontrolled dynamics. It is expected to find broad application, from guiding normal stem cells to differentiate into specific cell types such as cardiomyocytes, to normalizing diseased or aged cells, regenerating damaged tissue, modulating immune responses, and designing cancer reversion therapies that return cancer cells to a near-normal state.
Professor Cho said, "NUDGE is not a technology limited to returning a specific cell to a healthy state, but a general-purpose cell-control technology that guides a cell from its current state to a desired state through a single minimal stimulus." He added that the technology is expected to serve as a foundational tool for designing new control strategies across stem cell differentiation, aging, autoimmune disease, regenerative medicine, and cancer reversion therapy.
The study was conducted by Ferio Brahmana, who holds a master's degree, and Corbin Hopper, a PhD student, both in KAIST's Department of Bio and Brain Engineering, who contributed equally as co-first authors, with PhD student Woojeong Lee as a co-author. The findings were published online on September 14 in the Proceedings of the National Academy of Sciences of the United States of America (PNAS).
Paper Title: Uncovering minimal control of cell fate by natural dynamics,
DOI: 10.1073/pnas.2604777123
This work was supported by National Research Foundation of Korea grants funded by the Korea Government, the Ministry of Science and Information and Communication Technology (ICT) (RS-2023-NR077224, RS-2024-00405360, RS-2025-17652976, and 2021M3A9I4024447 [Bio & Medical Technology Development Program]). It was also supported by a grant of the Korea Dementia Research Project through the Korea Dementia Research Center, funded by the Ministry of Health & Welfare and the Ministry of Science and ICT, Republic of Korea (RS-2025-02264017) and by the Korea Advanced Institute of Science and Technology (KAIST) Quantum+X Convergence R&D Project and KAIST Grand Challenge 30 Project.
2026.09.23 View 3026 -
KAIST Extends Lifespan of Graphite-Free Anode-Free Batteries That Could Make EV Batteries Smaller and Lighter
Korean researchers have developed a technology that could make electric-vehicle (EV) batteries smaller and lighter while helping EVs travel farther on a single charge. A KAIST research team has proposed a new solution to a persistent problem in anode-free batteries, which reduce weight and volume by eliminating anode materials such as graphite that normally store lithium during charging. In these batteries, lithium can grow into sharp, spike-like structures that shorten battery life. The key lies in using nanofabrication techniques employed in semiconductor manufacturing to create uniform sites for lithium deposition and promote the formation of a robust protective layer.
KAIST (President Choongsik Bae) announced on September 22 that a joint research team led by Professors Jinwoo Lee and Hee-Tae Jung from the Department of Chemical and Biomolecular Engineering, working with researchers from Kyungpook National University (President Heo Young-woo) and the National NanoFab Center (President Park Heung Soo), has developed a new technology that extends the lifespan of anode-free batteries by applying the ultrafine fabrication processes used in semiconductor device manufacturing to batteries.
Conventional lithium-ion batteries store lithium in an anode material such as graphite during charging. Anode-free batteries, by contrast, deposit lithium directly onto a thin copper foil without a separate anode material. Because they eliminate the weight and volume that the anode material would otherwise occupy, they are drawing attention as a next-generation technology capable of packing more energy into a battery of the same size.
However, repeated charging and discharging cause lithium to deposit unevenly on the copper foil, growing into branch- or spike-like structures known as dendrites. The protective layer over the lithium also becomes unstable, causing the battery’s performance and lifespan to deteriorate rapidly. This is one of the most significant obstacles standing in the way of commercializing anode-free batteries.
Some previous approaches to extending the lifespan of anode-free batteries involved adding excess lithium or applying a thick protective layer to the surface. These methods provide extra lithium to compensate for losses during use or coat the surface with a protective film. However, adding more material makes the battery heavier and thicker, undercutting the very advantage of a small, lightweight anode-free battery.
To minimize added weight and volume, the research team modified the copper foil on which lithium is deposited. Much like marking out closely spaced parking bays in a flat lot, the team created an array of microscopic tube-shaped structures on the copper surface so that lithium spreads out and deposits evenly rather than concentrating in one spot.
To do this, the team used a process called secondary sputtering lithography (SSL), used for the precise micromachining of semiconductors. Fabricating microscopic tubes about 300 nanometers in diameter and 150 nanometers in height on the copper foil increased the surface area available for lithium deposition to about four times that of flat copper foil, helping to reduce localized, spike-like lithium growth.
The team then coated the copper foil with a roughly 10-nanometer-thick layer of MXene, a two-dimensional material, forming an ultrathin film only a few thousandths the thickness of a human hair. Rather than serving as a finished protective layer itself, the MXene acts like a primer that draws together the components needed to form one. In the LiPF6-based electrolyte used in the study, the MXene surface promotes the formation of a robust protective layer rich in lithium fluoride (LiF) as the battery operates.
This protective layer reduces unwanted reactions between the lithium and the electrolyte and also suppresses the spike-like growth of lithium. By examining electrode surfaces and cross sections while minimizing air exposure, the research team confirmed that a nanoscale protective layer forms uniformly along the MXene and identified the mechanism behind its formation. The analysis employed X-ray photoelectron spectroscopy (XPS), time-of-flight secondary ion mass spectrometry (ToF-SIMS), and transmission electron microscopy (TEM), among other techniques.
Professor Jinwoo Lee said, “This study shows how ultrafine fabrication techniques used in semiconductor manufacturing can create both uniform sites for lithium deposition and a stable protective layer without changing the bulk electrolyte formulation or adding excess lithium.” He added that the technology is expected to serve as a foundation for accelerating the commercialization of high-energy anode-free batteries by extending battery life while minimizing increases in weight and volume.
KAIST researchers Eunji Kim, Hyunju Jung, and Jinuk Kim participated as co-first authors on the study. The findings were published online in the international journal Advanced Functional Materials on September 1.
Paper title: Anion-Interactive Anode Interfaces for Stable Anode-Free Lithium Metal Batteries,
DOI 10.1002/adfm.77931
Author information: Eunji Kim (KAIST/National NanoFab Center, co-first author), Hyunju Jung (KAIST, co-first author), Jinuk Kim (KAIST, co-first author), Hee-Tae Jung (KAIST, corresponding author), Yonghee Lee (Kyungpook National University, corresponding author), Jinwoo Lee (KAIST, corresponding author)
This research was supported by the Nano&Material Technology Development Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT (RS-2026-25542167), as well as the Global Semiconductor Advanced Fab Utilization Project through the National Nano Fab Center (NNFC). This work was also supported by Semiconductor-Secondary Battery Interfacing Platform Technology Development Project of NNFC.
2026.09.22 View 2567 -
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 2098 -
KAIST Technology Behind Toothbrushes and Sportswear Advances into High-Performance Graphene Fiber Research
KAIST’s discovery of graphene oxide liquid crystals in 2011 has laid the foundation for 15 years of research worldwide, leading to technologies for producing graphene fibers with high strength and thermal conductivity. The fundamental materials research that helped bring antibacterial toothbrushes and functional sportswear to market is now advancing into materials for thermal management.
KAIST (President Choongsik Bae) announced on September 18 that Professor Sang Ouk Kim’s team in the Department of Materials Science and Engineering has published a commentary in the News & Views section of Nature Materials, highlighting recent advances in high-performance graphene fibers based on graphene oxide liquid crystals and their scientific significance.
Graphene oxide consists of graphene, a single layer of carbon atoms arranged in a honeycomb structure, with oxygen functional groups attached. Graphene is strong and conducts heat and electricity well, but its poor dispersibility in common solvent makes it difficult to process in liquid form. By contrast, the oxygen functional groups allow graphene oxide to disperse readily in water and other solvents, making it easier to formulate into inks, apply as coatings, or spin into fibers.
In 2011, Professor Kim’s team was the first in the world to report that graphene oxide dispersed in water above a certain concentration spontaneously forms a liquid-crystalline state in which its thin sheets align in a common direction.
Above critical concentration conditions, graphene oxide sheets randomly dispersed in water align like a scattered deck of cards arranged to face the same direction. This alignment allows graphene oxide to be drawn into long fibers while maintaining the sheets’ orientation along the fiber axis.
Following this discovery, researchers worldwide have developed methods for producing graphene fibers from graphene oxide liquid crystals. Conventional methods, however, have faced problems such as breaks in the liquid filament during drawing and insufficiently aligned or loosely packed graphene sheets, which leave voids and defects within the fibers. These problems have limited efforts to improve strength and thermal conductivity simultaneously.
In their commentary, Professor Kim’s team discussed recent research addressing these limitations and analyzed how research on graphene oxide liquid crystals could lead to technologies for manufacturing high-performance fibers.
Researchers at Zhejiang University in China recently dispersed graphene oxide in highly viscous glycerol, giving the dispersion viscoelastic properties similar to those of a polymer solution. This enabled “ultrahigh-ratio drawing” during wet spinning, allowing the graphene oxide dispersion to be stretched much further than before.
During this process, the graphene oxide sheets become more closely aligned along the fiber axis, reducing internal voids and defects. Subsequent heat treatment at high temperatures promotes the growth of large, aligned graphitic crystallites, producing lightweight, strong graphene fibers with high thermal and electrical conductivity.
The graphene fibers produced in this recent study were reported to have a tensile strength of up to 5.9 gigapascals (GPa) and a thermal conductivity of up to 1,720 watts per meter per kelvin (W/(m·K)). In simple terms, the fibers resist breaking when pulled and transfer heat quickly. These results show how denser packing and better alignment of graphene oxide sheets can improve both strength and thermal conductivity.
In the commentary, Professor Kim’s team highlighted how research on graphene oxide liquid crystals has progressed from observing a fundamental phenomenon to processing a two-dimensional material through flow and stretching, much like a polymer solution.
KAIST's core technology related to graphene oxide liquid crystals has already moved beyond the laboratory into consumer products. Antibacterial graphene toothbrushes incorporating technology from Materials CreationCo., Ltd., a faculty startup founded by Professor Kim, have sold more than 14 million units since their 2023 launch. Functional fibers made with GrapheneTex material were used in the uniforms of the Taekwondo demonstration team at the 2024 Paris Olympics. More recently, the material has been applied to functional golf apparel, other sportswear, and bedding, as the company pursues expansion into global markets.
“Since its discovery in 2011, graphene oxide liquid crystal has provided a foundation for research over worldwide that has now led to strong, high-performance graphene fibers that conduct heat efficiently,” said Professor Kim. “What makes this significant is that a single line of basic research has led to applications ranging from consumer products such as toothbrushes and functional clothing to advanced materials for electronics, mobility, and aerospace.”
The commentary was written by PhD candidates Jin Hyo Kim and Sujin Cha and Professor Sang Ouk Kim, all of KAIST’s Department of Materials Science and Engineering, and was published in the News & Views section of Nature Materials on September 8. (END)
Commentary title: Processing graphene fibres by viscous flow
DOI: 10.1038/s41563-026-02735-y
2026.09.18 View 2667 -
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 3918 -
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 2792