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KAIST Develops mRNA Platform That Remains Effective Even in Aging and Obesity
<(From Left) Dr. Subin Yoon, Ph.D candidate Hyeonggon Cho, Prof. Jae-Hwan Nam, Prof. Young-suk Lee>
Since the COVID-19 pandemic, mRNA vaccines have gained attention as a next-generation pharmaceutical technology. mRNA therapeutics work by delivering genetic instructions that enable cells to produce specific proteins for therapeutic effects. However, their efficacy has been reported to decline in elderly individuals or patients with obesity. To address this limitation, Korean researchers have newly designed a key regulatory region of mRNA that improves therapeutic protein production efficiency, developing a next-generation mRNA platform that maintains effectiveness even in aging and obesity conditions.
KAIST (President Kwang Hyung Lee) announced on the 10th of March that a joint research team led by Professor Young-suk Lee of the Department of Bio and Brain Engineering and Professor Jae-Hwan Nam of The Catholic University of Korea (President Jun-Gyu Choi) has developed a new mRNA platform by precisely designing the sequence of the 5′ untranslated region (5′UTR)*, a key regulatory region of mRNA.*5′ untranslated region (5′UTR): A region of mRNA that initiates and regulates protein production. The design of this region influences both the amount and speed of protein synthesis.
The research team analyzed large-scale bioinformatics datasets to identify 5′UTR sequences that enable proteins to be produced more efficiently across diverse cellular environments. When applied, the designed sequences significantly enhanced protein production and immune responses even in preclinical models of aging and obesity.
mRNA is a long single-stranded RNA molecule that serves as the blueprint for producing proteins required by the body. It consists of several components: the 5′UTR, which initiates and regulates the rate of protein production; the coding sequence (CDS), which contains the genetic information for a specific protein; the 3′ untranslated region (3′UTR), which helps maintain mRNA stability within cells; and the poly(A) tail, which further enhances stability and supports protein synthesis.
Among these components, the 5′UTR and 3′UTR do not determine the type of protein produced, but they play a critical role in regulating how efficiently the protein is synthesized. For this reason, these regions are receiving increasing attention as key bioengineering platforms for improving the performance of various mRNA therapeutics, including vaccines and treatments.
<Schematic Diagram of mRNA Therapeutic Design and Validation Using Bioinformatics>
To identify highly efficient 5′UTR sequences capable of promoting protein production across multiple tissues and cellular environments, the team conducted an integrated analysis of large-scale biological datasets. This included multiple analytical approaches such as RNA sequencing (RNA-seq) for analyzing gene activity across tissues, single-cell RNA sequencing (scRNA-seq) for examining gene expression at the individual cell level, and ribosome profiling (Ribo-seq) for measuring actual protein translation efficiency.
The researchers also focused on the fact that in aging or obesity conditions, cells often experience high levels of stress—particularly oxidative stress—which can reduce their ability to synthesize proteins. When the newly designed mRNA therapeutics were applied to preclinical models of aging and obesity, the results showed significantly improved protein production and immune responses compared with existing approaches. This research is expected to be applicable not only to mRNA vaccines but also to a wide range of biopharmaceutical technologies, including gene therapies and immunotherapies.
<Multimodal Bio–Big Data Analysis–Based mRNA Therapeutic Design (AI-Generated Image)>
Professor Young-suk Lee of KAIST Department of Bio and Brain Engineering stated, “This study identified a design strategy that enables mRNA to produce proteins more efficiently by analyzing large-scale biological data,” adding, “This technology will provide an important foundation for ensuring that mRNA vaccines and therapeutics remain effective even in environments where drug efficacy may decline, such as in elderly or obese patients.”
In this study, Dr. Subin Yoon from The Catholic University of Korea and doctoral candidate Hyeonggon Cho from KAIST participated as co-first authors. The research findings were published online on January 2 in the internationally renowned journal Molecular Therapy (IF = 12.0), a leading journal in gene and cell therapy.
(Paper title: ”Designing 5′UTR sequences improves the capacity of mRNA therapeutics in preclinical models of aging and obesity” DOI: https://doi.org/10.1016/j.ymthe.2025.12.060)
This research was supported by the Excellent Young Researcher Program and the Bio-Medical Technology Development Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT, the Infectious Disease Response Innovative Technology Support Program of the Ministry of Food and Drug Safety, and the Infectious Disease Prevention and Therapeutics Technology Development Program of the Korea Health Industry Development Institute.
2026.03.10 View 1363 -
Earth’s Safety Limit Already Exceeded… Carbon Emissions More Than Double the Planetary Boundary
<(From Left) Professor Haewon McJeon, Dr. Paul Wolfram>
Earth is not infinite. Pollution beyond certain levels threatens the climate and ecosystems. To prevent this, scientists have proposed “Planetary Boundaries,” defining the safe operating limits of the Earth system. A KAIST research team recalculated climate change and nitrogen pollution using the same standard and found that current carbon emissions already exceed the planet’s sustainable limit by more than double.
KAIST (President Kwang Hyung Lee) announced on the 6th of March that Professor Haewon McJeon of the Graduate School of Green Growth and Sustainability, in collaboration with Dr. Paul Wolfram’s team at the Pacific Northwest National Laboratory (PNNL) of the U.S. Department of Energy, recalculated the carbon dioxide emission boundary using an annual emissions (flow) framework rather than the traditional cumulative carbon stock framework.
Until now, climate change has been evaluated based on how much CO₂ accumulates in the atmosphere (stock). In contrast, nitrogen and phosphorus pollution have been assessed based on how much is emitted each year (flow). Because these problems were measured using different metrics, it was difficult to fairly compare their relative severity. The research team therefore recalculated carbon emissions using the same annual emissions framework used for nitrogen pollution.
Based on the condition of limiting the rise in global average temperature to within 1.5°C, the analysis showed that the Earth’s safe limit for annual CO₂ emissions is approximately 4–17 gigatons (Gt CO₂ per year). However, humanity’s current annual emissions amount to about 37 gigatons (Gt CO₂ per year). This level exceeds the Earth’s safe operating space by more than twofold.
Professor Haewon McJeon stated, “When carbon emissions are compared using the same framework as nitrogen pollution, the severity of climate change becomes much clearer,” adding, “This study helps place different environmental problems on the same analytical basis, which can contribute to setting clearer policy priorities.”
<Comparative Measurement of Planetary Boundaries and Proposal for Flow-Based Carbon Emission Limits>
<Scope and Sensitivity of Flow-Based Carbon Emission Limits>
He further emphasized, “The need for integrated strategies that simultaneously consider carbon, nitrogen, and phosphorus pollution is growing,” adding that global efforts toward decarbonization must accelerate further.
The study was jointly led by Professor Haewon McJeon and Dr. Paul Wolfram as co-corresponding authors, with Hassan Niazi, Page Kyle, and other researchers from PNNL participating as collaborators. The research results were published on February 16 in the international journal Nature Sustainability.
※ Paper title: “Ensuring consistency between biogeochemical planetary boundaries”
DOI: https://doi.org/10.1038/s41893-026-01770-6
This research was supported by the project “Development of an AI-Based Next-Generation Integrated Assessment Model for Climate–Human Interactions” funded by the Ministry of Science and ICT and the National Research Foundation of Korea.
In a Science commentary published on March 5 titled “Thirty-six solutions to stabilize Earth’s climate,” Professor McJeon revisited the progress of climate technologies over the past 20 years. He pointed out that although humanity has possessed many of the necessary technologies, they have not been implemented quickly enough, allowing the climate crisis to intensify. He also emphasized that the pace of decarbonization must accelerate to achieve carbon neutrality.
※ Commentary: “Thirty-six solutions to stabilize Earth’s climate”
Link: https://doi.org/10.1126/science.aed5212
2026.03.06 View 869 -
KAIST Develops Brain-Like AI… Thinks One More Time Even When Predictions Are Wrong
<(From left) Professor Sang Wan Lee, Myoung Hoon Ha, and Dr. Yoondo Sung>
Artificial intelligence now plays Go, paints pictures, and even converses like a human. However, there remains a decisive difference: AI requires far more electricity than the human brain to operate. Scientists have long asked the question, “How can the brain learn so intelligently using so little energy?” KAIST researchers have moved one step closer to the answer.
KAIST (President Kwang Hyung Lee) announced on the 29th that a research team led by Distinguished Professor Sang Wan Lee of the Department of Brain and Cognitive Sciences has developed a new technology that applies the learning principles of the human brain to deep learning, enabling stable training even in deep artificial intelligence models.
Our brain does not passively receive the world. Instead of merely perceiving what is happening in the present, it first predicts what will happen next and, when reality differs from that prediction, adjusts itself to reduce the difference (i.e., prediction error). This is similar to anticipating an opponent’s next move in Go and changing strategy if the prediction turns out to be wrong. This mode of information processing is known as “Predictive Coding.”
< Predictive Coding (PC) Module >
Scientists have attempted to apply this principle to AI, but encountered difficulties. As neural networks become deeper, errors tend to concentrate in specific layers or vanish altogether, repeatedly leading to performance degradation.
The research team mathematically identified the cause of this problem and proposed a new solution. The key idea is simple: instead of predicting only the final outcome, the AI is designed to also predict how its prediction errors will change in the future. The team refers to this as “Meta Prediction.” In simple terms, it is an AI that “thinks once more about its mistakes.” When this method was applied, learning proceeded stably in deep neural networks without halting.
<Analysis of Instability in Predictive Coding Model Errors>
The experimental results were also impressive. In 29 out of 30 experiments, the proposed method achieved higher accuracy than the current standard AI training method, backpropagation. Backpropagation is the representative learning method in which AI “goes backward by the amount of error and corrects it.”
Conventional AI training methods (backpropagation) require tightly interconnected layers, meaning the entire network must be computed and updated simultaneously. In contrast, this new approach demonstrates that, like the brain, large AI models can be effectively trained even when learning occurs in a distributed and partially independent manner.
<Performance Comparison of Predictive Coding Models>
This technology is expected to expand into various fields where power efficiency is critical, including neuromorphic computing, robot AI that must adapt to changing environments, and edge AI operating within devices.
Distinguished Professor Sang Wan Lee stated, “The key to this research is not simply imitating the structure of the brain, but enabling AI to follow the brain’s learning principles themselves,” adding, “We have opened the possibility of artificial intelligence that learns efficiently like the brain.”
This study was conducted with Dr. Myoung Hoon Ha as the first author and Professor Sang Wan Lee as the corresponding author. The paper was accepted to the International Conference on Learning Representations (ICLR 2026) and was published online on January 26.
※ Paper title: “Stable and Scalable Deep Predictive Coding Networks with Meta Prediction Errors”Original paper: https://openreview.net/forum?id=kE5jJUHl9i¬eId=e6T5T9cYqO
This research was supported by the Ministry of Science and ICT and the Institute of Information & Communications Technology Planning & Evaluation (IITP) through the Digital Global Research Support Program (joint research with Microsoft Research), the Samsung Electronics SAIT NPRC Program, and the SW Star Lab Program.
2026.03.06 View 1763 -
Opening the Door to B Cell-Based Cancer-Remembering Personalized Cancer Vaccines
< (From left) KAIST Professor Jung Kyoon Choi, Dr. Jeong Yeon Kim, and Dr. Jin Hyeon An >
Neoantigens are unique markers that distinguish only cancer cells. By adding B cell reactivity, cancer vaccines can move beyond one-time attacks and short-term memory to become a long-term immunity that "remembers" cancer, effectively preventing recurrence. KAIST’s research team has developed an AI-based personalized cancer vaccine design technology that makes this possible and optimizes anticancer effects for each individual.
KAIST announced on January 2nd that Professor Jung Kyoon Choi’s research team from the Department of Bio and Brain Engineering, in collaboration with Neogen Logic Co., Ltd., has developed a new AI model to predict neoantigens—a core element of personalized cancer vaccine development—and clarified the importance of B cells in cancer immunotherapy.
The research team overcame the limitations of existing neoantigen discovery, which relied primarily on predicting T cell reactivity, and developed an AI-based neoantigen prediction technology that integrally considers both T cell and B cell reactivity.
This technology has been validated through large-scale cancer genome data, animal experiments, and clinical trial data for cancer vaccines. It is evaluated as the first AI technology capable of quantitatively predicting B cell reactivity to neoantigens.
Neoantigens are antigens composed of protein fragments derived from cancer cell mutations. Because they possess cancer-cell specificity, they have gained attention as a core target for next-generation cancer vaccines. Companies like Moderna and BioNTech developed COVID-19 vaccines using the mRNA platforms they secured while advancing neoantigen-based cancer vaccine technology, and they are currently actively conducting clinical trials for cancer vaccines alongside global pharmaceutical companies.
However, current cancer vaccine technology is mostly focused on T cell-centered immune responses, presenting a limitation in that it does not sufficiently reflect the immune responses mediated by B cells.
In fact, the research team of Professors Mark Yarchoan and Elizabeth Jaffee at Johns Hopkins University pointed out in Nature Reviews Cancer in May 2025 that “despite accumulating evidence regarding the role of B cells in tumor immunity, most cancer vaccine clinical trials still focus only on T cell responses.”
The research team’s new AI model overcomes existing limitations by learning the structural binding characteristics between mutant proteins and B cell receptors (BCR) to predict B cell reactivity. In particular, an analysis of cancer vaccine clinical trial data confirmed that integrating B cell responses can significantly enhance anti-tumor immune effects in actual clinical settings.
< Schematic Background of the Technology >
Professor Jung Kyoon Choi stated, “Together with Neogen Logic Co., Ltd., which is currently commercializing neoantigen AI technology, we are conducting pre-clinical development of a personalized cancer vaccine platform and are preparing to submit an FDA IND* with the goal of entering clinical trials in 2027.” He added, “We will enhance the scientific completeness of cancer vaccine development based on our proprietary AI technology and push forward the transition to the clinical stage step-by-step.”
*FDA IND: The procedure for obtaining permission from the U.S. Food and Drug Administration (FDA) to conduct clinical trials before administering a new drug to humans for the first time.
Dr. Jeong Yeon Kim and Dr. Jin Hyeon An participated as co-first authors in this study. The research results were published in the international scientific journal Science Advances on December 3rd.
※ Paper Title: B cell–reactive neoantigens boost antitumor immunity, DOI: 10.1126/sciadv.adx8303
2026.01.02 View 2653 -
KAIST-KakaoBank Speeds Up 'Explainable AI' by 11 Times: "Boosts Financial AI Reliability
< (From left) Professor Jaesik Choi of the Kim Jaechul Graduate School of AI, Ph.D candidate Chanwoo Lee, Ph.D candidate Youngjin Park >
The research team led by Professor Jaesik Choi of KAIST's Kim Jaechul Graduate School of AI, in collaboration with KakaoBank Corp, announced that they have developed an accelerated explanation technology that can explain the basis of an Artificial Intelligence (AI) model's judgment in real-time. This research achievement significantly increases the practical applicability of Explainable Artificial Intelligence (hereinafter XAI) technology in fields requiring real-time decision-making, such as financial services, by achieving an average processing speed 8.5 times faster, and up to 11 times faster, than existing explanation algorithms for AI model predictions.
In the financial sector, a clear explanation for decisions made by AI systems is essential. Especially in services directly related to customer rights, such as loan screening and anomaly detection, regulatory demands to transparently present the basis for the AI model's judgment are increasingly stringent. However, conventional Explainable Artificial Intelligence (XAI) technologies required the repeated calculation of hundreds to thousands of baselines to generate accurate explanations, resulting in massive computational costs. This was a major factor limiting the application of XAI technology in real-time service environments.
To address this issue, Professor Choi's research team developed the 'ABSQR (Amortized Baseline Selection via Rank-Revealing QR)' framework for accelerating explanation algorithms. ABSQR noticed that the value function matrix generated during the AI model explanation process has a low-rank structure. It introduced a method to select only a critical few baselines from the hundreds available. This drastically reduced the computation complexity, which was previously proportional to the number of baselines, to be proportional only to the number of selected critical baselines, thereby maximizing computational efficiency while maintaining explanatory accuracy.
Specifically, ABSQR operates in two stages. The first stage systematically selects important baselines using Singular Value Decomposition (SVD) and Rank-Revealing QR decomposition techniques. Unlike existing random sampling methods, this is a deterministic selection method aimed at preserving information recovery, which guarantees the accuracy of the explanation while significantly reducing computation. The second stage introduces an amortized inference mechanism, which reuses the pre-calculated weights of the baselines through cluster-based search, allowing the system to provide an explanation for the model's prediction result in real-time service environments without repeatedly evaluating the model. The research team verified the superiority of ABSQR through experiments on various real-world datasets. Tests on standard datasets across five sectors—finance, marketing, and demographics—showed that ABSQR achieved an average processing speed 8.5 times faster than existing explanation algorithms that use all baselines, with a maximum speed improvement of over 11 times. Furthermore, the degradation of explanatory accuracy due to speed acceleration was minimized, maintaining up to 93.5% of the explanation accuracy compared to the baseline algorithm. This level is sufficient to meet the explanation quality required in real-world applications.
< ABSQR Framework Overview. (1) The baseline selection stage utilizes the low-rank structure of the value function matrix to select only a small number of key baselines, and (2) the accelerated search stage reuses the pre-calculated baseline weight coefficients based on clusters. This dramatically reduces the computation complexity, which was proportional to the number of baselines, to be proportional only to the number of selected key baselines. >
A KakaoBank official stated, "We will continue relentless research and development to enhance the reliability and convenience of financial services and introduce innovative financial technologies that customers can experience." Chanwoo Lee and Youngjin Park, co-first authors from KAIST, explained the significance of the research: "This methodology solves the crucial acceleration problem for real-time application in the financial sector, proving that it is possible to provide users with the reasons behind a learning model's decision in real-time." They added, "This research provides new insights into what constitutes unnecessary computation and the selection of important baselines in explanation algorithms, practically contributing to the improvement of explanation technology efficiency." This research, co-authored by PhD candidates Chanwoo Lee and Youngjin Park from the KAIST Kim Jaechul Graduate School of AI, and researchers Hyeongeun Lee and Yeeun Yoo from the KakaoBank Financial Technology Research Institute, was presented on November 12 at the 'CIKM 2025 (ACM International Conference on Information and Knowledge Management)', the world's highest-authority academic conference in the field of information and knowledge management. ※ Paper Title: Amortized Baseline Selection via Rank-Revealing QR for Efficient Model Explanation
※ Author Information:
※ Author Information: DOI: https://doi.org/10.1145/3746252.3761036
Co-First Authors: Chanwoo Lee (KAIST Kim Jaechul Graduate School of AI), Youngjin Park (KAIST Kim Jaechul Graduate School of AI), Hyeogeun Lee (KakaoBank), Yeeun Yoo (KakaoBank)
Co-Authors: Daehee Han (KakaoBank), Junho Choi (KAIST Kim Jaechul Graduate School of AI), Kunhyung Kim (KAIST Kim Jaechul Graduate School of AI)
Corresponding Authors: Nari Kim (KAIST Kim Jaechul Graduate School of AI), Jaesik Choi (KAIST Kim Jaechul Graduate School of AI)
Meanwhile, this research achievement was conducted through KakaoBank's industry-academia research project 'Advanced Research on Explainable Artificial Intelligence Algorithms in the Financial Sector' and the Ministry of Science and ICT/Institute for Information & Communications Technology Planning and Evaluation (IITP) supported project 'Development of Explainable Artificial Intelligence Technology Providing Explainability in a Plug-and-Play Manner and Verification of Explanation Provision for AI Systems.'
2025.12.12 View 2591 -
KAIST Predicts Human Group Behavior with AI! 1st Place at the World’s Top Conference… Major Success after 23 Years
<(From Left) Ph.D candidate Geon Lee, Ph.D candidate Minyoung Choe, M.S candidate Jaewan Chun, Professor Kijung Shin, M.S candidate Seokbum Yoon>
KAIST (President Kwang Hyung Lee) announced on the 9th of December that Professor Kijung Shin’s research team at the Kim Jaechul Graduate School of AI has developed a groundbreaking AI technology that predicts complex social group behavior by analyzing how individual attributes such as age and role influence group relationships.
With this technology, the research team achieved the remarkable feat of winning the Best Paper Award at the world-renowned data mining conference “IEEE ICDM,” hosted by the Institute of Electrical and Electronics Engineers (IEEE). This is the highest honor awarded to only one paper out of 785 submissions worldwide, and marks the first time in 23 years that a Korean university research team has received this award, once again demonstrating KAIST’s technological leadership on the global research stage.
Today, group interactions involving many participants at the same time—such as online communities, research collaborations, and group chats—are rapidly increasing across society. However, there has been a lack of technology that can precisely explain both how such group behavior is structured and how individual characteristics influence it at the same time.
To overcome this limitation, Professor Kijung Shin’s research team developed an AI model called “NoAH (Node Attribute-based Hypergraph Generator),” which realistically reproduces the interplay between individual attributes and group structure.
NoAH is an artificial intelligence that explains and imitates what kinds of group behaviors emerge when people’s characteristics come together. For example, it can analyze and faithfully reproduce how information such as a person’s interests and roles actually combine to form group behavior.
As such, NoAH is an AI that generates “realistic group behavior” by simultaneously reflecting human traits and relationships. It was shown to reproduce various real-world group behaviors—such as product purchase combinations in e-commerce, the spread of online discussions, and co-authorship networks among researchers—far more realistically than existing models.
< The process of generating group interactions using NoAH >
Professor Kijung Shin stated, “This study opens a new AI paradigm that enables a richer understanding of complex interactions by considering not only the structure of groups but also individual attributes together,” and added, “Analyses of online communities, messengers, and social networks will become far more precise.”
This research was conducted by a team consisting of Professor Kijung Shin and KAIST Kim Jaechul Graduate School of AI students: master’s students Jaewan Chun and Seokbum Yoon, and doctoral students Minyoung Choe and Geon Lee, and was presented at IEEE ICDM on November 18.
※ Paper title: “Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes” Original paper: https://arxiv.org/abs/2509.21838
< Photo from the award ceremony held on November 14 at the International Spy Museum in Washington, D.C.>
Meanwhile, including this award-winning paper, Professor Shin’s research team presented a total of four papers at IEEE ICDM this year. In addition, in 2023, the team also received the Best Student Paper Runner-up (4th place) at the same conference.
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-202400457882, AI Research Hub Project) (RS-2019-II190075, Artificial Intelligence Graduate School Program (KAIST)) (No. RS-2022-II220871, Development of AI Autonomy and Knowledge Enhancement for AI Agent Collaboration).
2025.12.09 View 3051 -
How Does AI Think? KAIST Achieves First Visualization of the Internal Structure Behind AI Decision-Making
<(From Left) Ph.D candidate Daehee Kwon, Ph.D candidate Sehyun lee, Professor Jaesik Choi>
Although deep learning–based image recognition technology is rapidly advancing, it still remains difficult to clearly explain the criteria AI uses internally to observe and judge images. In particular, technologies that analyze how large-scale models combine various concepts (e.g., cat ears, car wheels) to reach a conclusion have long been recognized as a major unsolved challenge.
KAIST (President Kwang Hyung Lee) announced on the 26th of November that Professor Jaesik Choi’s research team at the Kim Jaechul Graduate School of AI has developed a new explainable AI (XAI) technology that visualizes the concept-formation process inside a model at the level of circuits, enabling humans to understand the basis on which AI makes decisions.
The study is evaluated as a significant step forward that allows researchers to structurally examine “how AI thinks.”
Inside deep learning models, there exist basic computational units called neurons, which function similarly to those in the human brain. Neurons detect small features within an image—such as the shape of an ear, a specific color, or an outline—and compute a value (signal) that is transmitted to the next layer.
In contrast, a circuit refers to a structure in which multiple neurons are connected to jointly recognize a single meaning (concept). For example, to recognize the concept of cat ear, neurons detecting outline shapes, neurons detecting triangular forms, and neurons detecting fur-color patterns must activate in sequence, forming a functional unit (circuit).
Up until now, most explanation techniques have taken a neuron-centric approach based on the idea that “a specific neuron detects a specific concept.” However, in reality, deep learning models form concepts through cooperative circuit structures involving many neurons. Based on this observation, the KAIST research team proposed a technique that expands the unit of concept representation from “neuron → circuit.”
The research team’s newly developed technology, Granular Concept Circuits (GCC), is a novel method that analyzes and visualizes how an image-classification model internally forms concepts at the circuit level.
GCC automatically traces circuits by computing Neuron Sensitivity and Semantic Flow. Neuron Sensitivity indicates how strongly a neuron responds to a particular feature, while Semantic Flow measures how strongly that feature is passed on to the next concept. Using these metrics, the system can visualize, step-by-step, how basic features such as color and texture are assembled into higher-level concepts.
The team conducted experiments in which specific circuits were temporarily disabled (ablation). As a result, when the circuit responsible for a concept was deactivated, the AI’s predictions actually changed.
In other words, the experiment directly demonstrated that the corresponding circuit indeed performs the function of recognizing that concept.
This study is regarded as the first to reveal, at a fine-grained circuit level, the actual structural process by which concepts are formed inside complex deep learning models. Through this, the research suggests practical applicability across the entire explainable AI (XAI) domain—including strengthening transparency in AI decision-making, analyzing the causes of misclassification, detecting bias, improving model debugging and architecture, and enhancing safety and accountability.
The research team stated, “This technology shows the concept structures that AI forms internally in a way that humans can understand,” adding that “this study provides a scientific starting point for researching how AI thinks.”
Professor Jaesik Choi emphasized, “Unlike previous approaches that simplified complex models for explanation, this is the first approach to precisely interpret the model’s interior at the level of fine-grained circuits,” and added, “We demonstrated that the concepts learned by AI can be automatically traced and visualized.”
< Overview of the Conceptual Circuit Proposed by the Research Team >
This study, with Ph.D. candidates Dahee Kwon and Sehyun Lee from KAIST Kim Jaechul Graduate School of AI as co–first authors, was presented on October 21 at the International Conference on Computer Vision (ICCV).
Paper title: Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations
Paper link: https://openaccess.thecvf.com/content/ICCV2025/papers/Kwon_Granular_Concept_Circuits_Toward_a_Fine-Grained_Circuit_Discovery_for_Concept_ICCV_2025_paper.pdf
This research was supported by the Ministry of Science and ICT and the Institute for Information & Communications Technology Planning & Evaluation (IITP) under the “Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation” project, the AI Research Hub Project, and the KAIST AI Graduate School Program, and was carried out with support from the Defense Acquisition Program Administration (DAPA) and the Agency for Defense Development (ADD) at the KAIST Center for Applied Research in Artificial Intelligence.
2025.11.26 View 3301 -
Failure in the AI Era? The 3rd Failure Conference Held
< 2025 Failure Conference Poster >
KAIST announced on the 31st of October that it will be holding the '3rd Failure Conference' from Wednesday, November 5th to Friday, November 14th. The event is organized by the KAIST Center for Ambitious Failure (Director Sungho Jo), and, under the theme 'AI times Failure,' it will re-examine the value of humaneness through the sensibility of 'failure' in this era of great transformation led by AI technology.
Composed of lectures, competitions, exhibitions, and networking programs, this conference provides a venue for new introspection on the relationship between humanity, society, and technology through the lens of 'failure.'
Failure Seminar 'AI Era, Asking the Way of Humanity' will be held on November 6th at the Jeong Geun-mo Conference Hall in the Academic and Cultural Complex
Professor Juho Kim of the KAIST School of Computing will discuss the human sensibility and resilience needed in the AI era through the paradox that "AI learns how to fail less, but humans are losing the opportunity to fail. Following this, Professor Sang Wook Lee of the Hanyang University Department of Philosophy will present philosophical and ethical challenges and practical directions for the advancement of AI technology to lead to universal welfare for humanity. The 'AI times Failure Idea Contest' Finals will take place on November 7th at the John Hanner Hall in the Academic and Cultural Complex. 12 teams, selected from preliminaries that included 111 teams from universities and graduate schools nationwide, will demonstrate their ideas in booth form on the theme of 'The Future where AI and Humans Coexist.' Participants will explore AI errors, human limitations, and the possibility of trust and recovery, presenting attempts to convert technological failure into human introspection, and human failure into technological possibility. On the day of the finals, the Grand Prize (KAIST President’s Award), First Prize, and Second Prize will be selected through judging.
The Photography Exhibition '404: Perfection Not Found' will be held on the 1st floor of the Creative Learning Building from November 5th to 14th. This exhibition showcases 'Scenes of Imperfection' captured by KAIST members through the PhotoVoice program and the AI times Failure Snapshot Challenge. It is divided into three sections: ▲ Brain that Mimics Perfection: Failure of AI ▲ Incomplete Connection: Portrait of the Digital Generation ▲ Aesthetics of Imperfection: Warmth of Humanity, providing a space for introspection that illuminates human responsibility and potential through technological failure. The 'Show Off Your Failed Project Contest,' which has garnered great response from KAIST students every year, will be expanded to include general public participation on the 5th at the John Hanner Hall in the Academic and Cultural Complex. Co-planned by the KAIST Center for Ambitious Failure and the student club ICISTS, participants will decorate their own booths with photos and videos to share their failures and the process of overcoming them. Awards such as ▲ Best (Most Votes) ▲ Shining Debris Award (Highly Relatable Failure Story) ▲ Flower of Ash Award (Overcoming Story) ▲ Aesthetics of Failure Award (Creative Expression) ▲ Beautiful Afterimage Award (Sincere Lingering Impression) will be selected through audience voting.
< 2025 Show Off Your Failed Project Contest Poster >
Sungho Jo, KAIST Center for Ambitious Failure (Professor, School of Computing), stated, "As AI technology rapidly evolves and changes the order of the world, humans need to look back at themselves beyond that speed. I hope this Failure Conference will be an opportunity to rediscover the meaning of humaneness amid technological innovation and to imagine a better future." Kwang Hyung Lee, President of KAIST, said, "Failure is another name for challenge, and a seed of innovation. KAIST will lead the AI era and human-centered technological development through a creative spirit of challenge that is not afraid of failure."
All programs for the 2025 Failure Conference are open to anyone interested, and detailed schedules and content can be checked on the webstie of KAIST Center for Ambitious Failure (caf.kaist.ac.kr).
2025.10.29 View 2076 -
Robot-Operated Space Station Construction Goal... 'In-space Servicing and Manufacturing Research Center' Launched
<Plaque Handover Ceremony. (From left) Jae-Hung Han, Director of the Space Research Institute, Ju-won Kang, Head of Engineering Group at the National Research Foundation of Korea Basic Research Headquarters>
KAIST's Space Research Institute announced on the 24th of October that it officially launched the 'Innovative Research Center for the Development of Core Technologies in In-space Servicing and Manufacturing (ISMRC)' at the KAIST Academic Cultural Center on Friday, October 24. About 150 officials from major organizations, including the Korea Aerospace Administration, the National Research Foundation of Korea, and Daejeon Metropolitan City, as well as domestic and foreign space experts, attended the opening ceremony to discuss future cooperation measures. The 'KAIST In-space Servicing and Manufacturing Research Center (ISMRC)' is a large-scale research hub selected for the Ministry of Science and ICT's 2025 Basic Research Project, with a total of 71.2 billion KRW long-term project planned over the next 10 years, including 50 billion KRW in national funding. Daejeon City will also provide a total of 3.6 billion KRW, with 400 million KRW annually starting from 2026. The research goals are to secure core technologies for next-generation space exploration, including: ▲ Construction of Unmanned Space Stations, ▲ Robotics-based In-space Manufacturing, and ▲ Resource Recovery Technology. A team of 14 KAIST professors, led by Director Jae-Hung Han, will spearhead the research, with major domestic and foreign space companies and research institutions participating in joint research. As the 'New Space' era fully commences globally, the In-space Servicing and Manufacturing industry is projected to grow to tens of trillions of Korean won by 2030, driven by the reduction of launch costs and the expansion of private sector participation. This field is evaluated as a core area that will fundamentally change the way humanity engages in space activities, including extending satellite lifespan, on-orbit maintenance and operation, and securing and manufacturing resources in space. Meanwhile, an international symposium was held for two days on October 23-24 at the KAIST Academic Cultural Center and KI Building, coinciding with the opening ceremony.
<Director Jae-Hung Han of the Space Research Institute presenting>
The symposium was composed of a total of six sessions, including: ▲ Exchange Meeting on Additive Manufacturing Tecnology for Aerospace, ▲ International Workshop on Aerospace Composites, ▲ Workshop on Swarm Satellite Development, and ▲ Workshop on In-space Servicing and Manufacturing Robotics. Major domestic and foreign institutions and experts, including the Korea Aerospace Research Institute, Japan Advanced Institute of Science and Technology, and California Institute of Technology (Caltech), attended to discuss the future direction of next-generation space technology development and international cooperation measures. Cheol-woong Son, Director-General of Future Strategy Industry Office at Daejeon City, said, "We will develop the Innovative Research Center into a Daejeon-type space industry innovation platform with KAIST," and "Daejeon City will concentrate its capabilities to help local businesses grow and establish Daejeon as the central city for the Republic of Korea's space industry." Jae-Hung Han, Director of the KAIST Space Research Institute, said, "We will lead the core technologies for in-space servicing and manufacturing through cooperation between industry, academia, research institutes, and government, and contribute to the establishment of a private sector-focused industrial ecosystem," adding, "KAIST will grow into a comprehensive research hub that encompasses R&D, talent nurturing, and technology commercialization."
<Group Photo of Participants at the Opening Ceremony of the In-space Servicing and Manufacturing Research Center>
Kwang Hyung Lee, President of KAIST, said, "The field of in-space servicing and manufacturing is a core area that will change the paradigm of the future space industry," and "KAIST will lead the Republic of Korea to become the center for opening a new era of the space industry through innovative technology development and global cooperation." KAIST plans to perform the role of breaking down the boundaries between academia and industry, focusing on these technologies, and laying the foundation for next-generation space activities.
2025.10.24 View 4029 -
Thinking outside the box to Fabricate Customized 3D Neural Chips
<(From Left) Professor Yoonkey Nam, Dr. Dongjo Yoon from the Department of Bio and Brain Engineering>
Cultured neural tissues have been widely used as a simplified experimental model for brain research. However, existing devices for growing and recording neural tissues, which are manufactured using semiconductor processes, have limitations in terms of shape modification and the implementation of three-dimensional (3D) structures.
By "thinking outside the box," a KAIST research team has successfully created a customized 3D neural chip. They first used a 3D printer to fabricate a hollow channel structure, then used capillary action to automatically fill the channels with conductive ink, creating the electrodes and wiring. This achievement is expected to significantly increase the design freedom and versatility of brain science and brain engineering research platforms.
On the 25th, KAIST announced that a research team led by Professor Yoonkey Nam from the Department of Bio and Brain Engineering has successfully developed a platform technology that overcomes the limitations of traditional semiconductor-based manufacturing. This technology allows for the precise fabrication of "3D microelectrode array" (neural interfaces with multiple microelectrodes arranged in a 3D space to measure and stimulate the electrophysiological signal of neurons) in various customized forms for in vitro culture chips.
Existing 3D microelectrode array fabrication, based on semiconductor processes, has limited 3D design freedom and is expensive. While 3D printing-based fabrication techniques have recently been proposed to overcome these issues, they still have limitations in terms of 3D design freedom for various in vitro neural network structures because they follow the traditional sequence of "conductive material patterning → insulator coating → electrode opening."
The KAIST research team leveraged the excellent 3D design freedom provided by 3D printing technology and its ability to use printed materials as insulators. By reversing the traditional process, they established an innovative method that allows for more flexible design and functional measurement of 3D neuronal network models for in vitro culture.
<Schematic Diagram of an Integrated Cell Culture Substrate-Microelectrode Array Platform for In Vitro Cultured 3D Neural Network Models>
First, they used a 3D printer to print a hollow 3D insulator with micro-tunnels. This structure was designed to serve as a stable scaffold for conductive materials in 3D space while also supporting the creation of various 3D neuronal networks. They then demonstrated that by using capillary action to fill these internal micro-tunnels with conductive ink, they could create a 3D scaffold-microelectrode array with more freely arranged microelectrodes within a complex 3D culture support structure.
The new platform can be used to create various chip shapes, such as probe-type, cube-type, and modular-type, and supports the fabrication of electrodes using different materials like graphite, conductive polymers, and silver nanoparticles. This allows for the simultaneous measurement of multichannel neural signals from both inside and outside the 3D neuronal network, enabling precise analysis of the dynamic interactions and connectivity between neurons.
Professor Nam stated, "This research, which combines 3D printing and capillary action, is an achievement that significantly expands the freedom of neural chip fabrication." He added that it will contribute to the advancement of fundamental brain science research using neural tissue, as well as applied fields like cell-based biosensors and biocomputing.
Dr. Dongjo Yoon from KAIST's Department of Bio and Brain Engineering participated as the first author of the study. The research findings were published online in the international academic journal Advanced Functional Materials (June 25th issue).
※Paper Title: Highly Customizable Scaffold-Type 3D Microelectrode Array Platform for Design and Analysis of the 3D Neuronal Network In Vitro
This research was supported by the Consolidator Grants Program and the Global Basic Research Laboratory Program of the National Research Foundation of Korea.
2025.09.26 View 2659 -
The Secret of Our Success Author Joseph Henrich to Deliver Special Lecture at KAIST
KAIST announced on the 19th that its Institute for Mind and Brain Sciences and the Department of Brain and Cognitive Science will be hosting a special lecture by world-renowned cultural evolution scholar, Professor Joseph Henrich of Harvard University. The free lecture will take place on the 22nd at the Conference Room on the 1st floor of the Meta-Convergence Hall at the KAIST main campus, with support from the Gikwan Foundation. The event is open to the public.
Professor Henrich, a professor in the Department of Human Evolutionary Biology at Harvard, is a leading authority on the evolution of culture and cooperation. He was recognized for his work on the origins of human cooperative behavior through a comparative study of 15 small-scale societies, earning the 2024 Panmure House Prize* (Adam Smith 300th Anniversary Prize) and the 2022 Hayek Book Prize.
* Panmure House Prize: An academic award established in honor of Adam Smith's scholarship, named after the building where he lived.
< Poster for Special Lecture by Professor Joseph Henrich of Harvard University >
His representative books, "The WEIRDest People in the World" and "The Secret of Our Success," have created a significant stir in both academia and the general public by offering new interpretations of the formation and development of human society from a cultural evolution perspective.
"The WEIRDest People in the World" emphasizes that human thought and behavior are products of specific cultural environments rather than universal truths. "The Secret of Our Success" presents a new perspective on how humanity, through cultural artifacts like language, tools, and institutions, has achieved unique success compared to other animals.
The lecture will be divided into two sessions: an academic seminar and a public lecture. The academic seminar, held from 10:00 AM to 11:30 AM, will be conducted in English on the topic of "Cultural Evolutionary Psychology, Kinship, and the Historical Origins of Modern Psychological Differences." It is intended for researchers, graduate students, and undergraduate students in related fields.
Following this, a public lecture will be held from 3:00 PM to 5:00 PM on the topic of "The Collective Brain: Social and Cultural Origins of Creativity." Professor Jeong Jae-seung of KAIST's Department of Brain and Cognitive Science will serve as the moderator, and simultaneous interpretation will be provided.
The lecture will cover how innovation and creativity are products of a collective intelligence formed by diverse people exchanging ideas through networks. It will also discuss how the pace of innovation within a population is determined by key factors such as community size, social connectivity, and cognitive diversity, and how these principles explain innovation in various social contexts, including cultural psychology, immigration, urbanization, and institutions. There will also be a Q&A session with the author of "The Secret of Our Success."
Regarding the lecture, Professor Henrich stated, "In human evolution, culture is not just a backdrop; it's the core driving force that makes us human. Through this lecture, I want to share how we have learned from each other, cooperated, and developed knowledge and institutions. I especially look forward to having a deep conversation with the audience about the evolutionary significance of the passion for education and learning culture in Korean society."
Professor Jeong Jae-seung of KAIST's Department of Brain and Cognitive Science said, "This lecture was organized to explore how the human mind and brain have evolved through interaction with culture. It will be a valuable opportunity to hear the insights of a world-renowned scholar from the interdisciplinary perspective of meditation science and brain and cognitive science."
To register for the event, you can use the link (https://forms.gle/7TW9FAKv1qgA3dBBA) or the QR code on the poster. For inquiries, please contact the KAIST Institute for Mind and Brain Sciences at 042-350-1361.
2025.09.19 View 1955 -
Accurate Real time ECG Measurement While Comfortably Lying Down at Home
< (From left) Professor Chul Kim of the Department of Bio and Brain Engineering, Ph.D. candidate Minjae Kim, researcher Premravee Teeravichayangoon >
KAIST's research team has developed a technology that can measure electrocardiogram (ECG) and heart rate variability (HRV) in real time by simply lying on a bed with clothes on, without having to go to the hospital. This technology is expected to evolve into a daily heart health monitoring platform in conjunction with remote healthcare, and further expand into various bio-healthcare fields such as sleep and stress analysis, contributing to personalized prevention and early diagnosis for patients.
KAIST announced on the 19th that Professor Chul Kim's research team from the Department of Bio and Brain Engineering has developed an 'in-bed cardiac monitoring on-device system'.
The research team manufactured a flexible substrate sensor that integrates the electronic circuit and electrodes into one to increase precision, and implemented an integrated system that can perform signal-noise separation, heart beat signal (R-peak) detection, and heart rate variability analysis in real time through on-device signal processing.
Existing ECG measurement had the inconvenience of visiting a hospital, taking off clothes, and attaching wet electrodes to the skin. Because of this, long-term monitoring was difficult, and it was not easy for the elderly or patients with chronic diseases to use it daily. Non-contact methods also had a technical limitation of being vulnerable to external noise.
To solve these problems, the research team applied a circuit that blocks external noise (active shielding) and a circuit that stably captures minute current changes in the human body (right-leg drive circuit). In addition, they implemented a mathematical transformation technique (wavelet transform) that extracts only the important parts from the heart beat signal and a calculation method (peak detection algorithm) that accurately identifies the moment of the heart's electrical beat (R-peak) as on-device signal processing techniques, allowing for precise real-time analysis of the signal.
As a result, users can obtain stable and accurate ECG signals even when lying on their backs with clothes on.
< Figure. Overall structural diagram of the developed non-contact in-bed cardiac monitoring on-device system, schematic diagram of the R-peak detection algorithm, real-time ECG and HRV measurement screen >
This research presents new possibilities for managing chronic cardiovascular diseases and supporting the health of the elderly, as it can be easily used not only in hospitals but also at home.
Professor Chul Kim said, "This system, which can extract signals in real time even in a noisy environment, can be used to easily check heart health in daily life," and added, "In the future, it will become the foundation of sleep health management by adding the measurement of various bio-signals."
This paper, in which Ph.D. candidate Minjae Kim and researcher Premravee Teeravichayangoon from the Department of Bio and Brain Engineering participated as co-first authors, was published online in the international journal 'Biosensors and Bioelectronics' on August 9, 2025.
※ Paper title: A homecare in-bed hardware system for precise real-time ECG and HRV monitoring with layered clothing. DOI: https://doi.org/10.1016/j.bios.2025.117838
※ Author information: Minjae Kim (KAIST Department of Bio and Brain Engineering, First Author), Premravee Teeravichayangoon (KAIST Department of Bio and Brain Engineering, First Author), Chul Kim (KAIST Department of Bio and Brain Engineering, Corresponding Author)
Meanwhile, this research was carried out with the support of the National Research Foundation of Korea's Basic Research Lab and Bio-medical Technology Development Project, and the KAIST-Ceragem Future Healthcare Research Center.
2025.09.19 View 1922