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KAIST Global Entrepreneurship Summer School Marks Fifth Consecutive Year of Cultivating Future Entrepreneurs in Silicon Valley
The 2026 Global Entrepreneurship Summer School (GESS), organized by the KAIST Office of Global Initiatives, has successfully concluded its fifth annual program.
Now in its fifth year, GESS has become KAIST's flagship global entrepreneurship program, providing students with firsthand experience in Silicon Valley—the world's leading startup ecosystem—and equipping them with the entrepreneurial mindset and global competencies needed to launch ventures on the international stage.
Participants in the 2026 GESS program, including both undergraduate and graduate students, were selected through a competitive process consisting of document screening, interviews, team presentations, and peer evaluations.
Prior to traveling to Silicon Valley, the selected students completed a four-month preparatory program that included team building, customer discovery, business model development, and pitch preparation. Throughout the program, they received mentoring from entrepreneurs, venture investors, and industry experts, enabling them to refine their business ideas and evaluate their potential for entering global markets.
The Silicon Valley program, held in late June, was organized in collaboration with leading startup support organizations, including KOTRA Silicon Valley, IBK Changgong Silicon Valley, and Plug and Play. Through meetings with entrepreneurs, venture capitalists, and representatives from global technology companies, students gained firsthand insight into the Silicon Valley startup ecosystem while developing a deeper understanding of global markets.
For the fourth consecutive year, students from the KAIST College of Business Impact MBA program also participated in the Silicon Valley program, creating valuable opportunities for interdisciplinary collaboration and exchange among students with diverse academic backgrounds and professional experiences.
A highlight of this year's program was a startup storytelling workshop conducted in collaboration with educators from Stanford University. The workshop helped students strengthen their communication skills by learning how to present their ideas more persuasively—an essential competency for aspiring global entrepreneurs.
In partnership with KAIST alumni based in Silicon Valley, participants also visited leading global technology companies and unicorn startups, including Meta, NVIDIA, and Moloco. They attended networking events with local professionals and alumni, gaining firsthand exposure to the innovation culture and growth strategies of global technology companies while broadening their perspectives on international careers and entrepreneurship.
To put into practice one of the core values of entrepreneurship—creating positive social impact—GESS participants also organized "Let's Play AI+Tech," a community outreach program for elementary school students from underserved families in Sunnyvale, California. Designed and led entirely by KAIST students, the program introduced fundamental concepts in artificial intelligence through engaging, hands-on activities for children and their parents. The initiative also offered KAIST students a meaningful opportunity to give back to the local community while sharing their expertise in AI and technology.
The program concluded with the Final Pitch Competition, where each team presented the business models they had developed over several months to a panel of Silicon Valley investors and entrepreneurship experts. Through expert feedback and evaluation, participants had the opportunity to validate the global potential of their ventures.
Following a highly competitive final round, Team CUPID was named the overall winner. Team CUPID presented an AI-powered developer platform that automatically routes coding tasks to the most cost-effective AI model, significantly reducing developers' AI usage costs. The team received high praise from the judges for its clear problem definition, strong market potential, and scalability in the global market.
Gianidita Nurani Pertiwi, a member of Team CUPID and a student in the Department of Bio and Brain Engineering, said, "GESS provided an invaluable opportunity to experience Silicon Valley's entrepreneurial ecosystem firsthand. Through conversations with founders, investors, and industry experts, I learned how to refine our ideas from a global perspective. The experience has motivated me to continue pursuing innovation that can create meaningful impact beyond borders."
The 2026 GESS program has been organized for the fifth consecutive year by the Office of Global Initiative in collaboration with the Impact MBA program and the Startup KAIST. KAIST will continue strengthening partnerships with Silicon Valley and other global innovation hubs to nurture entrepreneurial talent capable of leading future industries worldwide.
2026.07.06 View 51 -
KAIST: Dementia-Causing Substance Turns On a Therapeutic “Switch”
A substance that worsens dementia has become a “switch” that initiates treatment. KAIST researchers have developed a new therapeutic approach that uses hydrogen peroxide (H₂O₂), a reactive oxygen species that damages cells and increases in the brains of patients with Alzheimer’s disease, to activate a drug selectively in diseased brain tissue. The team also confirmed improvements in cognitive function through animal experiments, presenting a new possibility for next-generation dementia treatment.
KAIST announced on the 2nd that a research team led by Professor Mi Hee Lim of the Department of Chemistry, in collaboration with Professor Mingeun Kim of Chonnam National University, Dr. Chul-Ho Lee and Dr. Kyoung-Shim Kim of the Korea Research Institute of Bioscience and Biotechnology, and Dr. Young-Ho Lee of the Korea Basic Science Institute, has developed a prodrug that is activated selectively in the diseased brain in Alzheimer’s disease and confirmed its therapeutic effects through animal experiments.
A prodrug is a drug that initially has minimal therapeutic effect but is converted into an active therapeutic agent only under specific conditions inside the body. In this study, the prodrug was designed to be activated only when it encounters hydrogen peroxide, which increases in the brains of patients with Alzheimer’s disease, allowing it to function as a “smart therapeutic agent” that selectively acts in diseased brain tissue.
In the brains of Alzheimer’s disease patients, hydrogen peroxide, which damages cells, is elevated above normal levels. Until now, it has generally been regarded only as a harmful substance that should be removed. However, the research team devised a method to use it instead as a signal that activates a drug.
The prodrugs developed by the research team, BE-1 and BE-2, are designed to remain minimally reactive in a healthy brain. However, when they encounter hydrogen peroxide in a brain affected by dementia, they are converted into active therapeutic compounds, AP-1 and AP-2. Through this process, they reduce reactive oxygen species, including hydrogen peroxide, while also preventing amyloid beta (Aβ) peptides — peptides known as a major cause of dementia that accumulate in the brain and damage nerve cells — from aggregating into highly toxic clumps.
Using advanced analytical techniques, the research team confirmed that the activated drug alters the morphology of amyloid beta aggregates and suppresses their growth into large aggregates.
These effects were also confirmed in Alzheimer’s disease mouse models. The drug crossed the blood-brain barrier (BBB), a protective barrier that controls whether substances in the blood can enter the brain, and was converted into the therapeutic compound inside the diseased brain. In mice that received long-term drug administration, oxidative stress in the hippocampus, which is responsible for memory, was reduced, and amyloid beta accumulation in the brain also decreased. In behavioral experiments assessing the ability to recognize new objects and navigate mazes, cognitive function was also found to improve.
This study is significant in that the drug was designed to operate only where needed by using the environment of the diseased brain itself. This approach presents a new strategy for dementia treatment that can enhance therapeutic efficacy while reducing side effects, and it is expected to be applicable to the treatment of other neurodegenerative diseases, such as Parkinson’s disease.
Professor Mi Hee Lim of KAIST’s Department of Chemistry said, “This study is meaningful in that hydrogen peroxide, which had previously been regarded only as something to be eliminated, was used as a signal to activate a drug. We expect this strategy, which activates drugs in diseased tissue, to become a new platform for treating complex diseases such as Alzheimer’s disease more safely and effectively.”
This study was co-first-authored by Jimin Lee and Eunseo Hong, Ph.D. candidates in KAIST’s Department of Chemistry, and was published online on May 31, 2026, in the international journal Small (Impact Factor: 12.1, top 10% in the field of chemistry).
※ Paper title: A Prodrug Approach for Activity-Based Chemical Modulation toward Multiple Pathological Targets in Alzheimer’s Disease
DOI: 10.1002/smll.74013
This research was supported by the National Research Foundation of Korea’s Leader Researcher Program, Global Leading Research Center Program, Sejong Science Fellowship, Graduate Student Research Encouragement Program, and institutional programs of KRIBB and KBSI.
2026.07.03 View 239 -
KAIST Develops AI That Reads Animal Behavior Like Language
An artificial intelligence model capable of reading and interpreting animal behavior like language has been developed by researchers at KAIST. The team created BehaVERT, an AI model that learns behavioral data in a manner similar to natural language and was able to independently identify social behavioral deficits in an autism mouse model, opening a new avenue for interpretable neuroscience.
KAIST (President Kwang-Hyung Lee) announced that a research team led by Professor Dae-Soo Kim from the Department of Brain and Cognitive Sciences has developed an AI model that interprets animal movements as a form of behavioral language.
The researchers transformed skeletal movements of mice into tokens, analogous to words in natural language, and trained a transformer-based model to learn behavioral meaning. The resulting model, named BehaVERT, successfully identified core social behavioral abnormalities in an autism mouse model without being provided any prior biological knowledge.
The study introduces a novel AI framework for analyzing animal behavior through language-based representations. Beyond simple behavior classification, the model demonstrates the ability to uncover biologically meaningful patterns and may serve as a foundation for next-generation behavioral foundation models applicable to drug discovery, psychiatric research, and behavioral genetics.
Inspired by the idea that animal behavior may possess structures similar to language, the researchers represented the positions of a mouse's nose, ears, spine, limbs, and tail as behavioral tokens and trained a BERT-based transformer architecture.
As a result, BehaVERT learned not only to classify behaviors but also to understand their contextual meaning over time, much like language models infer meaning from sequences of words.
The model achieved state-of-the-art performance across five international benchmark datasets covering social interaction, multi-animal behavior, three-dimensional motion analysis, and autism-related behavioral assessment.
Importantly, BehaVERT also provides interpretability, allowing researchers to visualize which behavioral cues influenced its decisions.
In experiments distinguishing Shank3B knockout autism-model mice from healthy controls, the AI consistently focused on oral-oral contact behavior. This finding aligns with previous biological studies showing that autism-model mice exhibit deficits in social interaction despite maintaining normal approach behavior.
In other words, the AI independently rediscovered a key biological characteristic solely from behavioral observations, without explicit biological instruction.
The researchers further found that the model's internal representation space organized behavioral features such as mobility, attention, and social engagement into structured patterns. This suggests that animal behavior, much like language, may possess an underlying semantic structure.
The study also highlights an unusual interdisciplinary achievement. The first author, Dr. Seungjae Shin, and other members of the research team were trained primarily in biology rather than artificial intelligence. By independently learning transformer architectures and deep learning techniques, they designed specialized models and training strategies tailored for behavioral analysis.
Professor Kim's laboratory has long pursued AI-driven behavioral analysis and previously developed AVATAR, a technology that reconstructs rodent behavior in virtual environments, leading to the founding of Actnova Inc.
"The project began with a simple question: Could animal movements contain a structure similar to language?" said Dr. Seungjae Shin, the first author of the study.
The team also adopted a self-supervised learning framework that enables AI to learn directly from behavioral data without manual annotations. Furthermore, a model trained on rat behavior successfully transferred to mouse behavior analysis, demonstrating the feasibility of a behavioral foundation model applicable across species.
"BehaVERT goes beyond behavior classification and enables the interpretation of behavioral meaning," said Professor Dae-Soo Kim. "We expect it to become a key research tool for discovering new insights in drug development, psychiatric disorders, behavioral genetics, and many other areas of life sciences."
The study was published on March 24, 2026, in the International Journal of Computer Vision (IJCV), one of the world's leading journals in computer vision.
Paper Information
• Title: BehaVERT: A Transformer-Based Motion Language Model for Decoding Behavioral Semantics in Mice
• Journal: International Journal of Computer Vision (IJCV)
• DOI: 10.1007/s11263-026-02834-y
Related Videos
• BehaVERT — Social Behavior Analysis Visualization (Investigation & Mount), https://youtu.be/JshCr-ZBQR0
• BehaVERT — Social Behavior Analysis Visualization (Investigation & Attack), https://youtu.be/p9RPhZM__Js
• BehaVERT — AI Discovers Core Social Behavioral Features in an Autism Mouse Model, https://youtu.be/D6zUyDu3t9I
Funding
This research was supported by the Mid-Career Researcher Program and the Brain Convergence Technology Development Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT), Republic of Korea.
2026.07.01 View 462 -
How Does Superconductivity Begin? Unveiling the Hidden Flow of Electrons
Superconductivity, a phenomenon where electricity flows without resistance, is considered the core of quantum computers and next-generation power technologies. However, the exact states electrons undergo before superconductivity emerges have not yet been fully elucidated. KAIST researchers have provided experimental clues revealing the hidden order electrons form prior to superconductivity in a kagome metal, a material closely related to superconducting phenomena. The team confirmed that a loop-like circulating order of electrons (loop-current order) emerges earlier than the periodic clustering of electrons (charge density wave).
KAIST (President Kwang Hyung Lee) announced on the 30th that a joint research team led by Professors Yeongkwan Kim, Myung Joon Han, and SungBin Lee from the Department of Physics discovered through circular dichroism angle-resolved photoemission spectroscopy (CD-ARPES) experiments and theoretical calculations that time-reversal symmetry breaking occurs at a higher temperature than the charge density wave formation in the kagome metal CsV3Sb5. Time-reversal symmetry is a property where physical phenomena appear identical even when time is reversed. The breaking of this symmetry implies that electrons within the material may have created a hidden flow with a specific directionality.
A kagome metal is a material with a repeating triangular atomic arrangement, resembling the traditional Japanese basket weaving pattern 'kagome'. In this structure, electrons interact strongly with each other, giving rise to various quantum phenomena rarely seen in normal metals, such as charge density waves, superconductivity, and topological electronic states. In particular, CsV3Sb5 exhibits both charge density waves and superconductivity at low temperatures, drawing attention as a crucial platform for next-generation quantum materials research.
However, there has been an ongoing debate over whether another hidden electronic order exists between the charge density wave and superconductivity in this material. Although several experiments have reported signals suggesting broken time-reversal symmetry, it was unclear whether this phenomenon was a consequence of the charge density wave formation or an independent electronic order that emerges prior to it.
To resolve this debate, the research team alternately irradiated high-quality CsV3Sb5 single crystals with left- and right-circularly polarized light and precisely measured the difference in the intensity of the emitted electrons. They then eliminated spurious signals potentially caused by the experimental setup's geometry, isolating only the intrinsic signals originating from the symmetry breaking of the material itself.
As a result, they confirmed that the signal of time-reversal symmetry breaking already appears around 140~145 K, which is significantly higher than the charge density wave formation temperature of about 94 K. This supports the interpretation that electrons form a loop-current order—a microscopic loop-like circulation—before creating the charge density wave pattern. The loop-current order is an electronic order where electrons behave as if flowing along small loops within the atomic lattice; it was theoretically proposed long ago but has been difficult to verify experimentally.
The team also tracked how the electronic structure changed as the temperature was lowered. At high temperatures, a normal metallic state appeared; at lower intermediate temperatures, the loop-current order formed first. As the temperature decreased further, a complex state evolved where the charge density wave intertwined with the loop-current order, eventually leading to the superconducting state. This research proposes a hierarchical structure of phase transitions in CsV3Sb5, progressing from 'loop-current order → charge density wave → superconductivity'.
This achievement provides a crucial clue for understanding the fundamental principles of superconductivity. It is not yet fully understood what kind of order electrons form before superconductivity occurs, or which electronic orders compete or cooperate with superconductivity. By demonstrating the existence of an electronic state with broken time-reversal symmetry prior to the superconducting state, this study offers an important lead in understanding unconventional superconductivity, which operates differently from standard mechanisms.
Furthermore, this research is expected to help understand hidden electronic orders in other superconducting materials beyond kagome metals. In particular, it could serve as a reference for explaining the peculiar electronic state (pseudogap) prior to superconductivity, which has long been discussed in cuprate high-temperature superconductors.
Professor Yeongkwan Kim stated, "This research is the result of directly tracking the time-reversal symmetry breaking of a kagome metal within its electronic structure, which had previously only been discussed through indirect signals. By showing the sequence in which electrons form order before reaching superconductivity, we have presented a new reference point for research on unconventional superconductivity and strongly correlated quantum materials.“
Professor Myung Joon Han added, "The key point is that the circular dichroism signal observed in the experiment aligns perfectly with the electrons' orbital motion pattern (orbital angular momentum pattern) expected from the loop-current order. This is a case where we uncovered the microscopic origin of the hidden electronic order by combining experiment and theory.“
KAIST Department of Physics researchers Jaehun Cha, Hyunggeun Lee, and Sangjun Sim participated as co-first authors in this study. The research findings were published online in the international physics journal Nature Physics on June 15, 2026.
Paper Title: Evidence of time-reversal symmetry breaking above the charge density wave order in a kagome metal
DOI: https://doi.org/10.1038/s41567-026-03331-2
This research was supported by the Mid-Career Researcher Program and the Accelerator Manpower Training Program (Ministry of Science and ICT, National Research Foundation of Korea), the Korea Research Institute of Standards and Science (KRISS), the Air Force Office of Scientific Research (AFOSR), and the US Department of Energy's Basic Energy Sciences (DOE BES).
2026.07.01 View 806 -
KAIST Identifies New Therapeutic Target by Revealing How Cancer ‘Hijacks’ the Blueprint for Blood Vessel Development
Anti-angiogenic therapies targeting VEGF have been widely used in cancer treatment, yet their long-term efficacy remains limited. Tumor vascular endothelial cells (TECs) exhibit high adaptive plasticity, enabling them to resist treatment and sustain tumor growth, but the molecular mechanism underlying this plasticity has remained poorly understood.
KAIST, led by President Kwang Hyung Lee, announced that a joint research team led by Professor Inkyung Jung (Department of Biological Sciences), Professor Ji Min Lee (Graduate School of Medical Science and Engineering), and Professor Gou Young Koh (Institute for Basic Science) has now uncovered the answer. By integrating cross-cancer single-cell transcriptomic and epigenomic atlases across eight solid tumor types with multiomic profiles, including 3D chromatin contact maps, of human embryonic stem cell (hESC)-derived vascular endothelial cell differentiation, the team demonstrated that TECs reactivate a gene regulatory program normally confined to the late progenitor stage of vascular development. Much like reusing an old blueprint rather than drawing up a new one, tumors co-opt this pre-existing developmental program to fuel blood vessel growth.
The team’s integrative framework combined single-cell RNA-seq and ATAC-seq across multiple tumor types with H3K27ac ChIP-seq, Hi-C-based 3D chromatin mapping across a dense time series of hESC-to-EC differentiation. This approach resolved the EC-progenitor specific regulatory program that defines the shared pro-angiogenic program between late EC progenitors and TECs.
Within this framework, integrin receptor (ITGAV) emerged as a functional mediator specifically upregulated in both late EC progenitors and TECs. Cell-to-cell interaction analysis identified multiple key ligands from tumor micro enviroment (TME) that reactivate the progenitor-associated gene regulatory program. Pharmacologic inhibition attenuated endothelial migration, invasion, and tube formation in vitro, and significantly reduced tumor vascularization and growth in a colorectal cancer xenograft model in vivo.
Professor Inkyung Jung noted that this study reframes how we understand tumor angiogenesis: tumors do not invent new mechanisms, but exploit regulatory programs already embedded in normal vascular development. This insight offers a new conceptual basis for why anti-VEGF therapies face limitations, and points toward targeting the underlying regulatory architecture of endothelial plasticity as a complementary anti-angiogenic strategy.
The study was co-first authored by Dr. Andrew J. Lee, Dr. Sunwoo Min, Ph.D. student Su Chan Park; and Dr. Mei-Yu Qiu. Professors Inkyung Jung, Ji Min Lee, and Gou Young Koh served as corresponding authors. The findings were published on June 8 in Cancer Research [IF = 22.3].
※ Paper title: "A Co-opted Developmental Gene Regulatory Program in Endothelial Progenitors Promotes Tumor Angiogenic Phenotypes"
※ DOI: 10.1158/0008-5472.CAN-25-5094
※ Authors: Andrew J. Lee (KAIST, first author), Sunwoo Min (KAIST, co-first), Su Chan Park (KAIST, co-first), Mei-Yu Qiu (IBS, co-first), Gou Young Koh (IBS, co-corresponding), Ji Min Lee (KAIST, co-corresponding), Inkyung Jung (KAIST, corresponding)
This research was supported by the National Research Foundation of Korea and the Institute for Basic Science.
2026.07.01 View 241 -
KAIST Identifies Hidden Age Bias in Artificial Intelligence
Do responses generated by artificial intelligence systems such as ChatGPT reflect social prejudice? A KAIST research team has quantitatively analyzed and identified age-related stereotypes embedded in the responses of generative artificial intelligence. The study sheds light on the potential impact of hidden AI biases on social perceptions and suggests directions for the development of more inclusive AI.
KAIST, led by President Kwang Hyung Lee, announced on the 28th that a research team led by Professor Moon Choi of the Graduate School of Science and Technology Policy quantitatively analyzed subtle stereotypes about older adults embedded in sentences generated by OpenAI’s generative AI model ChatGPT-4o.
Generative AI is now widely used in everyday information search and decision-making processes, but concerns have also been raised that it may reproduce social biases contained in its training data. While previous studies have primarily focused on biases related to gender or race, this study, conducted by Ph.D. student Wan Hong as the first author, is significant in that it examined ageism from the perspective of artificial intelligence at a time when the issue is becoming increasingly important amid global population aging. Ageism refers to discrimination against, or negative perceptions of, certain groups based on age.
The research team collected 900 text samples generated by GPT-4o using neutral prompts that asked the model to describe the characteristics of age groups from 10 to 90 in 10-year intervals. The team then analyzed the responses using the Stereotype Content Model, a major theory in social psychology that explains perceptions of people or groups along two dimensions: warmth and competence.
The analysis found that older adults, defined as those aged 60 and above, received high scores for “warmth,” a trait associated with kindness, trustworthiness, and consideration. However, their scores for “competence,” which refers to ability, expertise, and efficiency, tended to be relatively lower than those of younger age groups.
The generated responses also tended to portray the human life course as divided into three groups: youth, covering those in their teens and 20s; middle age, covering those in their 30s to 50s; and older adulthood, covering those in their 60s and above. In particular, descriptions of people aged 70 and older repeatedly showed relatively uniform characteristics.
The research team also focused on “assertiveness,” which refers to the tendency to actively express one’s opinions and act with confidence and initiative. The analysis showed that the frequency of expressions related to assertiveness decreased as age increased. This suggests that ChatGPT-4o tends to portray older adults as wise and caring, while representing their agency and active capacities as relatively lower.
This study is significant because it quantitatively identified subtle biases embedded in generative AI by combining social science theory with computational analysis techniques. The findings show that generative AI tends to portray older adults as a “warm but less competent” group, a pattern similar to typical stereotypes of older adults repeatedly found in mass media.
This study is significant because it quantitatively identified subtle biases embedded in generative AI by combining social science theory with computational analysis techniques. The findings show that generative AI tends to portray older adults as a “warm but less competent” group, a pattern similar to typical stereotypes of older adults repeatedly found in mass media.
“Bias in AI is not merely a technological issue, but a social one,” said Professor Moon Choi. “To build inclusive artificial intelligence, people from diverse generations must participate in the development process.”
The study was conducted with Ph.D. student Wan Hong of the Graduate School of Science and Technology Policy as the first author. The findings were published in the February 2026 special issue of The Gerontologist, a leading international journal in the field of gerontology with an impact factor of 5.7.
※ Paper title: “An Exploratory Semantic Analysis of Age-Related Stereotypes in OpenAI’s GPT-4o Model”
※ DOI: https://doi.org/10.1093/geront/gnaf291
This research was supported by the National Research Foundation of Korea through the Mid-Career Research Program for Convergence between Science and Technology and the Humanities and Social Sciences.
※ Research team homepage: https://aging.kaist.ac.kr
2026.07.01 View 247 -
Crude Oil Separates Without Boiling: KAIST and Georgia Tech Develop Energy-Saving Membrane Technology
An international research team led by KAIST has developed a membrane technology that could significantly reduce the energy required for crude oil refining by replacing part of the century-old distillation process.
KAIST(President Kwang Hyung Lee) announced that a team led by Professor Dong-Yeun Koh of KAIST, in collaboration with Professor Ryan Lively's group at Georgia Tech, demonstrated a simple and inexpensive membrane capable of separating crude oil at room temperature without heating. The research was published in Nature, one of the world's leading scientific journals.
Crude oil underpins modern life by providing not only transportation fuels but also essential feedstocks for plastics, packaging materials, textiles, and countless consumer products. Because the cost of refining directly influences the price of these products, technologies that reduce refining energy consumption can generate substantial economic and environmental benefits.
Traditionally, refineries separate crude oil through distillation, a process that heats crude oil above 350 °C to vaporize it and then cools the vapor to recover different fractions. Globally, crude oil distillation consumes approximately 1,100 terawatt-hours (TWh) of energy each year—equivalent to the annual output of about 130 nuclear power plants, each at gigawatt scale, operating continuously. As a result, distillation remains one of the largest sources of energy consumption and greenhouse gas emissions in the refining industry.
At the same time, increasing cost pressures in global petrochemical markets have intensified the need for more energy-efficient separation technologies.
Membrane-based crude oil fractionations have attracted increasing attention as a potential alternative. However, conventional wisdom has held that molecularly precise separation requires an ultrathin selective layer coated onto the membrane surface. While effective, such coatings increase manufacturing costs and are prone to defects when scaled to large areas, limiting industrial deployment.
To overcome this challenge, the researchers took a radically different approach. Instead of relying on a specialized coating, they passed crude oil directly through a bare porous polyacrylonitrile (PAN) membrane—a chemically stable and inexpensive polymer commonly used as a support material in industrial membranes.
As crude oil permeated through the membrane, heavy hydrocarbons selectively deposited on the pore walls, gradually narrowing the pores and creating self-assembled separation channels smaller than 2 nanometers. Rather than relying on a specially engineered coating, the crude oil itself created the nanoscale pathways needed for precise molecular separation.
Through these self-formed channels, lighter fractions such as naphtha, gasoline, and kerosene permeated rapidly, while heavier components were effectively retained. In a surprising reversal, membrane fouling—normally regarded as a performance-degrading phenomenon—became the very mechanism that enabled highly selective separation.
The bare PAN membrane delivered crude oil permeation rates approximately 23 times higher than those of previously reported state-of-the-art crude oil membranes while maintaining stable performance for 28 consecutive days.
Professor Ryan Lively (Georgia Tech) commented “one of the key challenges facing membrane systems for crude oil separation was the low productivities of the membrane units – the PAN membranes with their surprising separation mechanism – dramatically increase the productivity of the membrane unit, to the point where industry should seriously consider adopting the technology.”
Importantly, the technology can be integrated into existing refinery infrastructure as a modular filtration unit, avoiding major equipment replacement and reducing barriers to industrial adoption.
Process simulations showed that using the membrane as a pretreatment step before conventional distillation could reduce energy consumption by 31.6%, carbon dioxide emissions by 37.6%, cooling water usage by 20.7%, and operating costs by 36%.
If adopted throughout Korea's refining and petrochemical sector, the technology could reduce greenhouse-gas emissions by approximately 10 million tonnes annually—equivalent to the emissions of roughly four million internal combustion vehicles.
Beyond crude oil refining, the membrane platform could be applied to a broad range of chemical separation processes, including the purification of pyrolysis oil derived from waste plastics, the recovery of solvents used in battery manufacturing, pharmaceutical purification, and biofuel production. The researchers believe the technology could serve as a versatile platform for next-generation molecular separations across multiple industries.
Professor Dong-Yeun Koh of KAIST said, “This study reveals a new scientific principle in which a membrane interacts with a complex mixture and spontaneously forms its own separation channels. Working with real crude oil supplied by HD Hyundai Oilbank allowed us to validate the technology under conditions relevant to industrial operation.”
Professor Jae W. Lee of KAIST, a co-corresponding author of the study, added, “By advancing large-area membrane modularization and long-term operational reliability, we hope to broaden the adoption of membrane-based processes throughout the refining and petrochemical industries.”
Dr. Jihoon Choi and Dr. Hyeokjun Seo of KAIST, the study’s co-first authors, said, “Our goal is to precisely control this spontaneous pore-constriction phenomenon and develop it into a membrane platform applicable to the entire refining process. We also aim to expand the technology to plastic recycling, biofuel purification, and other sustainable chemical processes that support carbon neutrality.”
The study was co-first-authored by Dr. Jihoon Choi and Dr. Hyeokjun Seo of KAIST and was published online in Nature on June 24, 2026.
Paper Title: Crude Oil Fractionation by Means of Mesoporous Polyacrylonitrile Membranes
DOI 10.1038/s41586-026-10677-3
https://www.nature.com/articles/s41586-026-10677-3
This research was supported by the Ministry of Science and ICT of Korea through the Basic Research Program for Outstanding Early-Career Researchers and the Engineering Research Center (ERC) Program.
2026.06.25 View 1012 -
KAIST Begins Developing the World’s First Brain-to-Robot Technology, Moving Robots by Thought and Sending Sensation Back to the Brain
KAIST researchers have begun developing a next-generation brain-robot interface platform that uses human brain signals to control an exoskeleton in real time and sends the tactile and force information sensed by the robot back to the brain.
KAIST, led by President Kwang-Hyung Lee, announced on the 25th that research teams led by Professors Kyoungchul Kong and Jung Kim of its Department of Mechanical Engineering, together with Angel Robotics Co., Ltd., have launched the world’s first bidirectional “Brain-to-Robot” system as a flagship initiative of the Korea Medical Device Development Fund (KMDF). The project runs from April 2026 to December 2032.
Professor Kyoungchul Kong is a world-renowned wearable-robotics researcher who founded Angel Robotics, a developer of walking-assist exoskeletons, and led his team to back-to-back gold medals at Cybathlon, the international competition for assistive technologies for people with disabilities. Professor Jung Kim is a globally recognized researcher who received the Scientist and Engineer of the Month Award for his work on robotic skin. Together, the two teams have formed a consortium to develop a Brain-to-Robot platform that merges neural interfaces with exoskeleton robotics.
Brain interface technologies that let users move a cursor or operate a smartphone with brain signals have already reached the stage of human clinical trials, and U.S. companies such as Neuralink and Synchron are accelerating their development. Existing approaches, however, have struggled to link actual movement and sensory feedback at the same time. They have also concentrated largely on advancing signal decoding itself, without clearly defining the target of control, namely what the brain signals actually drive and what kind of sensory information is returned.
Brain-to-Robot is designed to overcome these limitations head-on. It sets the exoskeleton itself as the control target: brain signals read the user’s movement intentions to drive the robot, and at the same time the robot’s sensory readings are delivered back to the brain. These readings include ground reaction force (the force the floor exerts on the foot), joint torque (rotational force at the joints), and tactile information. The aim is a complete bidirectional interface.
According to the research team, no fully bidirectional Brain-to-Robot system that combines exoskeleton control with sensory feedback has yet been reported anywhere in the world, and the project is expected to mark a turning point in brain interface technology.
Within this system, the KAIST teams are responsible for the core technologies. Professor Kong’s team will develop wearable-robot control and AI-based interpretation of movement intention, and will design a somatosensory interface, a system for transmitting bodily sensory information, that delivers the robot’s sensory data accurately to the Brain Chip, the semiconductor that processes brain signals.
Professor Kim’s team will develop robotic skin that senses in place of impaired sensation for people with disabilities, along with AI-based interpretation of somatosensory information.
The teams will also develop AI-based encoding and decoding algorithms that turn brain signals into robot commands and send the robot’s sensory information back to the brain. A key challenge is processing hundreds of channels of cortical signals, the neural signals generated in the cerebral cortex, while stably maintaining an ultra-low-latency closed loop, a control cycle in which signals are exchanged continuously in real time.
Commercialization of the flagship project will be led by Angel Robotics (KOSDAQ: 455900), the company founded by Professor Kong. The team plans to pursue full-cycle commercialization, from regulatory approval by the Ministry of Food and Drug Safety through to real-world deployment.
“If this technology succeeds, it will open a new rehabilitation paradigm in which people with quadriplegia can move beyond the hospital to walk on their own, pick up objects, and even feel sensation at their fingertips in everyday life,” Professor Kong said.
The research team stressed that, because this is an unprecedented and highly complex convergence technology never attempted at home or abroad, long-term safety, clinical validation, and a regulatory approval framework must advance in parallel with the technology itself. To reach the global market, they added, safety and efficacy testing, the accumulation of clinical evidence, a risk-management system, protection of brain-signal data, cybersecurity, and ethical review must all be addressed in an integrated way.
Meanwhile, KAIST is conducting a wide range of fundamental research in the field of brain interfaces. A research team led by Professor Hyung-Soon Park of the Department of Mechanical Engineering is studying wearable rehabilitation robot technologies based on neural intention-recognition interfaces, which identify users’ movement intentions from brain signals, for the effective treatment of neurological disorders. A research team led by Professor Sungho Cho of the School of Computing is developing AI-based brain-signal interpretation technologies.
A research team led by Professor Jihoon Lee of the Department of Brain and Cognitive Sciences is conducting next-generation brain–machine interface research focused on ultra-low-power bio/neural interface circuits, which connect and process biological and neural signals with low power consumption; wireless neural signal measurement technologies, which measure neural signals without wires; and on-device AI-based closed-loop neuromodulation technologies, which use cyclical control structures to exchange signals in real time.
In addition, a research team led by Professor Hyunjoo Lee of the School of Electrical Engineering is conducting research on high-resolution neural signal measurement and precision brain stimulation based on ultra-miniaturized multimodal neural electrodes, which can simultaneously measure and stimulate multiple types of neural signals. A research team led by Professor Minkyu Je of the Department of AI Semiconductor Systems is studying AI-based semiconductor integrated circuits and system technologies for next-generation neural interfaces. A research team led by Professor Jae-Woong Jeong of the School of Electrical Engineering is conducting research on high-precision brain-signal measurement, which precisely measures neural signals generated in the brain, and neuroengineering based on neural stimulation.
“This Brain-to-Robot flagship project is a world-class, highly challenging convergence research initiative led by the teams of Professors Kyoungchul Kong and Jung Kim,” said KAIST President Kwang-Hyung Lee. “KAIST has a wide range of researchers studying fundamental technologies in brain interfaces, AI, semiconductors, and robotics, and based on this foundation, we will lead innovation in next-generation Brain-to-Robot technologies.”
2026.06.25 View 938 -
KAIST Develops Robot Learning Technology Capable of Precisely Imitating Even “Rough” Demonstrations
Robots with increasingly precise dexterity are becoming essential in everyday life and industrial settings, from assembling tiny smartphone components to assisting doctors in surgery. However, teaching robots delicate human movements has traditionally required collecting vast amounts of data at extremely fine time intervals, resulting in significant costs and time burdens. KAIST researchers have developed a robot artificial intelligence technology that can perform sophisticated tasks by autonomously adjusting precision according to the situation, even when trained only on coarsely (sparsely) sampled demonstrations.
KAIST, led by President Kwang Hyung Lee, announced on the 24th that a research team led by Professor Daehyung Park of the School of Computing has developed DiSPo, a multi-granularity manipulation model that generates fine-grained robot motions tailored to a user’s desired level of precision, even from rough human demonstrations.
Existing robot learning methods, such as Behavior Transformer and Diffusion Policy, are limited by their dependence on the time intervals of the data used during training. As a result, learning precision manipulation tasks such as screw fastening or component insertion has required collecting large volumes of high-frequency data at very short time intervals. This has significantly increased data collection costs and slowed down the inference speed of robot AI models.
To overcome these limitations, the research team combined Mamba, a state-space model capable of predicting time intervals, with a diffusion model that enables rich action representation. The team also introduced a new Step-scale factor mechanism, which allows users to directly control the time intervals used by the robot.
As a result, even when trained on only low-frequency (coarse) demonstration data, the robot can generate high-precision motions during inference without additional training by autonomously subdividing actions through a discretization process.
DiSPo achieved up to an 81% higher task success rate compared to state-of-the-art models in simulation environments. In real-world experiments using a collaborative robot, DiSPo stably performed challenging tasks such as passing a clamp through a narrow gap with only a 2.5 mm radial clearance and accurately pressing a small shutter button on a smartphone. This performance was up to four times higher than that of existing AI models.
The technology is expected to make a significant contribution to automation in a wide range of everyday and industrial service fields that require high precision, including precision component assembly, cable connection, medical surgery, and precision machining.
“This study demonstrates that robots can learn precise motions from coarse demonstrations and autonomously adjust their level of precision according to the task situation,” said Professor Daehyung Park. “Moving forward, this technology is expected to dramatically reduce data collection costs while serving as a general-purpose robot learning technology for various industrial fields, including precision assembly and medical applications.”
The study was led by Nayoung Oh, a master’s student at the KAIST Graduate School of AI, as the first author, and was presented on June 1 at the 2026 IEEE International Conference on Robotics and Automation, or ICRA 2026, one of the world’s most prestigious robotics conferences, held in Vienna, Austria.
Paper Title: DiSPo: Diffusion-SSM based Policy Learning for Coarse-to-Fine Action Discretization
DOI: https://doi.org/10.48550/arXiv.2409.14719
2026.06.24 View 694 -
KAIST Team Wins IEEE RA-L Best Paper Award for Second Year in a Row
A research team led by Professor Jee-Hwan Ryu of the Department of Civil and Environmental Engineering at KAIST has received the Best Paper Award from IEEE Robotics and Automation Letters (RA-L), one of the world's most prestigious journals in robotics, for the second consecutive year.
KAIST(President Kwang Hyung Lee) announced on June 22 that Professor Ryu's team received the award during ICRA 2026, held in Vienna, Austria, with the award ceremony taking place on June 4. Following its recognition in 2025, the team has now achieved the rare distinction of winning the RA-L Best Paper Award in two consecutive years, underscoring the global competitiveness and sustained impact of KAIST's soft robotics research.
The RA-L Best Paper Award is presented annually to a select group of papers demonstrating outstanding scientific contribution, technical originality, experimental rigor, and future impact. This year, only five papers were selected from more than 1,700 papers published in IEEE Robotics and Automation Letters during 2025.
The award-winning paper, titled “Self-Wearing Adaptive Garments via Soft Robotic Unfurling,” presents a novel assistive dressing technology in which garments autonomously unfold and move along the user's body using soft robotic principles. The research was led by Dr. Namgyun Kim of KAIST in collaboration with the research group of Professor Allison M. Okamura at Stanford University.
Conventional dressing-assistance systems often rely on external robotic devices and complex control systems, which can restrict user movement and comfort. In contrast, the KAIST team incorporated the core principles of soft growing robots into garment structures, enabling the clothing itself to gently unfold and assist the wearer without requiring a separate external robotic manipulator.
The researchers integrated a pneumatic eversion mechanism into lightweight and flexible garments. When pressurized, the structure gradually unfolds along the user's body, providing reliable dressing assistance while maintaining safety and compliance. The soft robotic architecture is particularly advantageous for applications involving direct human contact, as it minimizes physical burden and reduces the need for sophisticated control systems.
The team developed and evaluated multiple garment prototypes, including sleeves, jackets, and pants. Experimental results demonstrated that the garments could reliably unfold along the user's body, reducing physical effort during dressing while maintaining safe interaction forces.
This work highlights how soft growing robotics can expand beyond traditional applications such as locomotion, exploration, and manipulation to directly support activities of daily living. The technology has strong potential for assistive applications for older adults, people with disabilities, and rehabilitation patients, while also opening new directions for wearable robotics, rehabilitation engineering, and human-robot interaction.
“This second consecutive award demonstrates the sustained global competitiveness of KAIST robotics research,” said Professor Jee-Hwan Ryu. “Our work extends the core principles of soft growing robots into assistive dressing technologies that can directly improve daily life. We will continue developing safe, flexible, and human-centered robotic technologies.”
The paper was published in IEEE Robotics and Automation Letters on November 19, 2025.
Paper Title: Self-Wearing Adaptive Garments via Soft Robotic Unfurling DOI: 10.1109/LRA.2025.3634909
2026.06.22 View 947 -
KAIST Teams Win Both International Challenges at ICRA 2026 and CVPR 2026
Two research teams from KAIST have claimed first place in international challenge competitions held at the world’s premier robotics and computer vision conferences.
KAIST (President Kwang-Hyung Lee) announced that the ACDC-K Team and the Curaytor Team, both from the laboratory of Prof. Hyun Myung in the School of Electrical Engineering, won first place in international challenge competitions held in conjunction with the IEEE International Conference on Robotics and Automation (ICRA 2026) and the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026), respectively.
The achievement highlights the global competitiveness of KAIST’s robotic perception and spatial intelligence technologies, with two teams from the same laboratory securing victories in leading international competitions across distinct research fields.
The ACDC-K Team won first place among more than 60 participating teams in the SLAM (Simultaneous Localization And Mapping) category of the Hilti×Trimble SLAM Challenge 2026, held during the Open Challenges in Robotics for Asset Inspection (OCRAIM) Workshop at ICRA 2026 in Vienna, Austria, from June 1 to 5.
Jointly organized by Hilti, Trimble, and the University of Oxford, the challenge evaluates robotic localization and mapping performance using sensor data collected from real construction sites. Participants were required to address practical challenges frequently encountered in construction environments, including non-overlapping front and rear fisheye camera configurations, low-texture indoor scenes, and rapid camera motion.
To tackle these challenges, the ACDC-K Team developed a robust visual-inertial SLAM system that fuses front and rear fisheye camera data with inertial measurements. By integrating feature-point and feature-line observations with adaptive constraints and correction mechanisms, the team achieved highly reliable localization and mapping performance in complex construction environments.
Meanwhile, the Curaytor Team won first place among eight participating teams in the Nothing Stands Still (NSS) Challenge 2026, held during the Computer Vision for the Built World (CV4AEC) Workshop at CVPR 2026 in Denver, Colorado, from June 3 to 7.
Jointly organized by Stanford University, ETH Zurich, and Oregon State University, the NSS Challenge evaluates 3D point cloud registration technologies for construction and industrial environments that evolve over time.
The Curaytor Team developed a novel multi-registration framework capable of aligning multiple LiDAR scans collected across different times and locations. The framework integrates feature extraction, correspondence estimation, robust global registration, registration confidence assessment, and change-aware refinement techniques. As a result, the team achieved highly accurate registration performance even in environments containing structural changes and dynamic objects.
“This achievement demonstrates the robustness of our visual-inertial SLAM and 3D LiDAR registration technologies in complex and constantly changing real-world environments,” said Prof. Hyun Myung. “It is particularly meaningful that our students secured first-place finishes in highly competitive international challenges hosted at two of the world’s most prestigious conferences in robotics and computer vision.”
Prof. Hyun Myung’s laboratory has consistently demonstrated excellence in spatial intelligence research. The laboratory previously won first place in the LiDAR track and ranked first among academic teams in the vision track of the Hilti SLAM Challenge in 2023. In addition, the Curaytor Team successfully defended its title in the NSS Challenge, securing back-to-back championships in 2025 and 2026.
2026.06.19 View 1543 -
KAIST Develops Next-Generation Database Technology That Reduces AI Hallucinations and Improves Accuracy by 78%
One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is “hallucination” — the generation of plausible-sounding but factually incorrect information. KAIST researchers have developed a next-generation database technology capable of understanding documents, data, and relationships among entities all at once. The technology improves AI response accuracy by up to 78% and processing speed by up to 20 times, addressing a key challenge in the commercialization of enterprise AI.
KAIST, led by President Kwang Hyung Lee, announced on the 19th that a research team led by Professor Min-Soo Kim of the School of Computing, in collaboration with faculty startup GraphAI Co., Ltd., has developed “AkasicDB,” a next-generation database technology that integrates the functions of vector databases, graph databases, and relational databases into a single database management system (DBMS). Based on this technology, the team also developed a new Retrieval-Augmented Generation (RAG) method called “Omni RAG.”
AkasicDB is designed to integrate and execute the functions of vector databases, which convert the meaning of documents or images into numerical vectors to search for similar information; graph databases, which store and analyze relationships among entities such as people, companies, and products; and relational databases, which systematically manage data in table form. Omni RAG, developed on this foundation, improves the accuracy of generative AI responses by simultaneously utilizing semantic information from documents, relationships among entities, and structured data.
AI agents have recently been spreading rapidly based on RAG technology, which searches vast collections of corporate documents and expert knowledge and generates responses based on the retrieved information. However, real-world enterprise data is distributed across various forms, including documents, tables, and relationships among entities, making it difficult for AI to comprehensively understand and use the data. As a result, AI may generate factually incorrect responses without sufficient grounding, creating hallucination issues that have been regarded as a major obstacle to the broader adoption of enterprise AI.
Conventional RAG typically works by converting user queries and documents into vectors, retrieving semantically similar documents, and providing them to a large language model (LLM), an AI model trained on massive datasets to generate human-like language. While this approach is effective for searching unstructured documents, it has limitations when handling complex queries that must also consider relationships among entities in documents or structured conditions such as specific periods, types, or ranges.
For example, a query such as, “Find clauses related to Company A among contracts signed last year, and explain how those clauses are connected to product supply issues,” requires vector search to identify document meaning, graph search to explore relationships among entities, and relational queries to filter by date and type. In existing systems, this required building multiple types of databases separately and combining the results at the application layer, leading to management complexity and response delays.
To solve this problem, the research team proposed Omni RAG, which integrates vector similarity search, graph traversal, and relational filtering within a single query and execution plan. Omni RAG identifies more accurate evidence by simultaneously utilizing semantic information from documents, relationship information from knowledge graphs, and structural conditions from tabular data, significantly reducing AI hallucinations.
AkasicDB, developed to support this method, adopts a new architecture that integrates graph databases, vector databases, and relational databases into a single engine. Users can express complex RAG queries that combine vector search, graph traversal, and relational filtering as a single SQL/GQL* query, and AkasicDB optimizes and processes the query as one unified execution plan.
SQL/GQL, or Structured Query Language/Graph Query Language, refers to command languages used to search or modify information stored in databases. SQL is the traditional language used to handle tabular data, while GQL is a language dedicated to graph data and is used to analyze connections among entities such as people, companies, and products.
Through this integrated architecture, AkasicDB minimizes unnecessary intermediate result generation and data movement, greatly reducing the number of tokens used by LLMs and shortening response latency. In experiments, complex search queries that took up to 21.3 seconds in existing systems were processed in under one second, achieving a performance improvement of more than 20 times. Omni RAG also improved response accuracy by up to 78% compared with conventional RAG. These results demonstrate its potential to substantially mitigate hallucination, one of the core challenges for enterprise AI agents.
Professor Min-Soo Kim said, “For AI agents to accurately understand and utilize the vast amounts of data held by enterprises, data infrastructure capable of processing vector, graph, and relational data in an integrated manner within a single system is essential. AkasicDB is a next-generation database technology for the era of AI agents, and we expect it to be used as core data infrastructure in fields requiring high reliability, including defense, manufacturing, finance, law, science, and technology.”
KAIST School of Computing Ph.D. student Geonho Lee participated in this research as the first author. The research results were presented as a demo paper on June 2 at ACM SIGMOD 2026, one of the world’s most prestigious international conferences in the field of databases, where they drew strong interest from global companies and researchers.
※ Paper title: AkasicDB: Demonstrating Omni RAG with a Unified Vector-Graph-Relational DBMS
DOI: https://doi.org/10.1145/3788853.3801609
※ Author information: Geonho Lee, KAIST, first author; Jeongho Park and Donghyoung Han, GraphAI Co., Ltd., co-authors; Professor Min-Soo Kim, KAIST, corresponding author
※ Demonstration video: https://www.youtube.com/watch?v=KD6MznZ61P4
2026.06.19 View 1177