Kaist

Direct Printing of Nanolasers, the Key to Optical ..

  < (From left) Professor Ji Tae Kim (KAIST), Dr. Shiqi Hu (First Author, AI-based Intelligent Design-Manufacturing Integrated Research Group, KAIST-POSTECH), and Professor Junsuk Rho (POSTECH) >       In future high-tech industries, such as high-speed optical computing for massive AI, quantum cryptographic communication, and ultra-high-resolution augmented reality (AR) displays, nanolasers—which process information using light—are gaining significant attention as core components for next-generation semiconductors. A research team at our university has proposed a new manufacturing technology capable of high-density placement of nanolasers on semiconductor chips, which process information in spaces thinner than a human hair. KAIST announced on January 6th that a joint research team, led by Professor Ji Tae Kim from the Department of Mechanical Engineering and Professor Junsuk Rho from POSTECH (President Seong-keun Kim), has developed an ultra-fine 3D printing technology capable of creating "vertical nanolasers," a key component for ultra-high-density optical integrated circuits. Conventional semiconductor manufacturing methods, such as lithography, are effective for mass-producing identical structures but face limitations: the processes are complex and costly, making it difficult to freely change the shape or position of devices. Furthermore, most existing lasers are built as horizontal structures lying flat on a substrate, which consumes significant space and suffers from reduced efficiency due to light leakage into the substrate. To solve these issues, the research team developed a new 3D printing method to vertically stack perovskite, a next-generation semiconductor material that generates light efficiently. This technology, known as "ultra-fine electrohydrodynamic 3D printing," uses electrical voltage to precisely control invisible ink droplets at the attoliter scale (＄10∧{-18}＄ L). Through this method, the team successfully printed pillar-shaped nanostructures—much thinner than a human hair—directly and vertically at desired locations without the need for complex subtractive processes (carving material away). The core of this technology lies in significantly increasing laser efficiency by making the surface of the printed perovskite nanostructures extremely smooth. By combining the printing process with gas-phase crystallization control technology, the team achieved high-quality structures with nearly single-crystalline alignment. As a result, they were able to realize high-efficiency vertical nanolasers that operate stably with minimal light loss. Additionally, the team demonstrated that the color of the emitted laser light could be precisely tuned by adjusting the height of the nanostructures. Utilizing this, they created laser security patterns invisible to the naked eye—identifiable only with specialized equipment—confirming the potential for commercialization in anti-counterfeiting technology. < 3D Printing of Perovskite Nanolasers > Professor Jitae Kim stated, "This technology allows for the direct, high-density implementation of optical computing semiconductors on a chip without complex processing. It will accelerate the commercialization of ultra-high-speed optical computing and next-generation security technologies." The research results, with Dr. Shiqi Hu from the Department of Mechanical Engineering as the first author, were published online on December 6, 2025, in ACS Nano, an international prestigious journal in the field of nanoscience. Paper Title: Nanoprinting with Crystal Engineering for Perovskite Lasers DOI: https://doi.org/10.1021/acsnano.5c16906 This research was conducted with support from the Ministry of Science and ICT’s Excellent Young Researcher Program (RS-2025-00556379), the Mid-career Researcher Support Program (RS-2024-00356928), and the InnoCORE AI-based Intelligent Design-Manufacturing Integrated Research Group (N10250154).      

KAIST Solves Key Commercialization Challenges of N..

  <(From Left) Ph.D candidate Juhyun Lee, Postdoctoral Researcher Jinuk Kim, (Upper Right) Professor Jinwoo Lee>   Anode-free lithium metal batteries, which have attracted attention as candidates for electric vehicles, drones, and next-generation high-performance batteries, offer much higher energy density than conventional lithium-ion batteries. However, their short lifespan has made commercialization difficult. KAIST researchers have now moved beyond conventional approaches that required repeatedly changing electrolytes and have succeeded in dramatically extending battery life through electrode surface design alone. KAIST (President Kwang Hyung Lee) announced on the 4th of January that a research team led by Professors Jinwoo Lee and Sung Gap Im of the Department of Chemical and Biomolecular Engineering fundamentally resolved the issue of interfacial instability—the greatest weakness of anode-free lithium metal batteries—by introducing an ultrathin artificial polymer layer with a thickness of 15 nanometers (nm) on the electrode surface. Anode-free lithium metal batteries have a simple structure that uses only a copper current collector instead of graphite or lithium metal at the anode. This design offers advantages such as 30–50％ higher energy density compared to conventional lithium-ion batteries, lower manufacturing costs, and simplified processes. However, during the initial charging process, lithium deposits directly onto the copper surface, rapidly consuming the electrolyte and forming an unstable solid electrolyte interphase (SEI), which leads to a sharp reduction in battery lifespan. Rather than changing the electrolyte composition, the research team chose a strategy of redesigning the electrode surface where the problem originates. By forming a uniform ultrathin polymer layer on the copper current collector using an iCVD (initiated chemical vapor deposition) process, they found that this layer regulates interactions with the electrolyte, precisely controlling lithium-ion transport and electrolyte decomposition pathways. <Figure 1. Schematic of an ultrathin artificial polymer layer (15 nm thick) introduced onto the electrode surface> In conventional batteries, electrolyte solvents decompose to form soft and unstable organic SEI layers, causing non-uniform lithium deposition and promoting the growth of sharp, needle-like dendrites. In contrast, the polymer layer developed in this study does not readily mix with the electrolyte solvent, inducing the decomposition of salt components rather than solvents. As a result, a rigid and stable inorganic SEI is formed, simultaneously suppressing electrolyte consumption and excessive SEI growth. Using operando Raman spectroscopy and molecular dynamics (MD) simulations, the researchers identified the mechanism by which an anion-rich environment forms at the electrode surface during battery operation, leading to the formation of a stable inorganic SEI. This technology requires only the addition of a thin surface layer without altering electrolyte composition, offering high compatibility with existing manufacturing processes and minimal cost burden. In particular, the iCVD process enables large-area, continuous roll-to-roll production, making it suitable for industrial-scale mass production beyond the laboratory. <Figure 2. Design rationale of the current collector-modifying artificial polymer layer and the SEI formation mechanism> Professor Jinwoo Lee stated, “Beyond developing new materials, this study is significant in that it presents a design principle showing how electrolyte reactions and interfacial stability can be controlled through electrode surface engineering,” adding, “This technology can accelerate the commercialization of anode-free lithium metal batteries in next-generation high-energy battery markets such as electric vehicles and energy storage systems (ESS).” This research was conducted with Ph.D candidate Juhyun Lee, and postdoctoral Jinuk Kim, a postdoctoral researcher from the Department of Chemical and Biomolecular Engineering at KAIST, serving as co–first authors. The results were published on December 10, 2025, in Joule, one of the most prestigious journals in the field of energy.  ※ Paper title: “A Strategic Tuning of Interfacial Li⁺ Solvation with Ultrathin Polymer Layers for Anode-Free Lithium Metal Batteries,”  Authors: Juhyun Lee (KAIST, co–first author), Jinuk Kim (KAIST, co–first author), Jinwoo Lee (KAIST, corresponding author), Sung Gap Im (KAIST, corresponding author), among a total of 18 authors, DOI: 10.1016/j.joule.2025.102226 This research was conducted at the Frontier Research Laboratory, jointly established by KAIST and LG Energy Solution, and was supported by the National Research Foundation of Korea (NRF) Mid-Career Research Program, the Korea Forest Service (Korea Forestry Promotion Institute) Advanced Technology Development Program for High Value-Added Wood Resources, and the KAIST Jang Young Sil Fellowship Program.    

KAIST Demonstrates Potential to Predict Drug Side ..

  <(From Left) Dr.Jaesang Kim, Professor Seongyun Jeon>   Rhabdomyolysis is a condition in which muscle damage—often caused by drug intake—can lead to impaired kidney function and acute kidney failure. However, there have been limitations in directly observing how muscle and kidney damage influence each other simultaneously within the human body. KAIST researchers have developed a new device that can precisely reproduce such inter-organ interactions in a laboratory setting. KAIST (President Kwang Hyung Lee) announced on the 5th of January that a research team led by Professor Seongyun Jeon of the Department of Mechanical Engineering, in collaboration with Professor Gi-Dong Sim’s team from the same department and Professor Sejoong Kim of Seoul National University Hospital, has developed a biomicrofluidic system that can recreate, in the laboratory, the process by which drug-induced muscle damage leads to kidney injury.  ＊Microfluidic system: a device that reproduces human organ environments on a very small chip This study is particularly significant in that it is the first to precisely reproduce, in a laboratory environment, the cascade of inter-organ reactions in which drug-induced muscle injury leads to kidney damage, using a modular (assembly-type) organ-on-a-chip platform that allows muscle and kidney tissues to be both connected and separated. To recreate conditions similar to those in the human body, the research team developed a structure that connects three-dimensionally engineered muscle tissue with proximal tubule epithelial cells (cells that play a key role in kidney function) on a single small chip. The system is a modular microfluidic chip that allows organ tissues to be connected or disconnected as needed. Cells and tissues are cultured on a small chip in a manner similar to real human organs and are designed to interact with one another. In this device, muscle and kidney tissues can be cultured separately under their respective optimal conditions and connected only at the time of experimentation to induce inter-organ interactions. After the experiment, the two tissues can be separated again for independent analysis of changes in each organ. A key feature of the system is that it allows quantitative evaluation of the effects of toxic substances released from damaged muscle on kidney tissue. <Figure 1. Conceptual Image of the Microfluidic System Experiment (Generated by AI)> Using this platform, the researchers applied atorvastatin (a cholesterol-lowering drug) and fenofibrate (a triglyceride-lowering drug), both of which are known clinically to induce muscle damage. As a result, the muscle tissue on the chip showed reduced contractile force and structural disruption, along with increased levels of biomarkers indicative of muscle damage—such as myoglobin＊ and CK-MM＊＊—which are characteristic changes seen in rhabdomyolysis.  ＊Myoglobin: a protein found in muscle cells that stores oxygen and is released into the blood or culture medium when muscle is damaged  ＊CK-MM (Creatine Kinase-MM): an enzyme abundant in muscle tissue, with higher levels detected as muscle cell destruction increases At the same time, kidney tissue exhibited a decrease in viable cells and an increase in cell death, along with a significant rise in the expression of NGAL＊ and KIM-1＊＊, biomarkers that increase during acute kidney injury. Notably, the researchers were able to observe the stepwise cascade in which toxic substances released from damaged muscle progressively exacerbated kidney injury.  ＊NGAL: a protein that rapidly increases when kidney cells are damaged  ＊KIM-1: a protein that becomes highly expressed as kidney cells—particularly proximal tubule cells—are increasingly damaged <Figure 2. Configuration of the Muscle–Kidney-on-a-Chip (MKoaC) Platform and Analysis of Drug Responses> Professor Seongyun Jeon stated, “This study establishes a foundation for analyzing the interactions and toxic responses occurring between muscle and kidney in a manner closely resembling the human body,” adding, “We expect this platform to enable the early prediction of drug side effects, identification of the causes of acute kidney injury＊, and further expansion toward personalized drug safety assessment.” ＊Acute kidney injury: a condition in which the kidneys suddenly lose their ability to function properly over a short period of time This research, with Jaesang Kim participating as the first author, was published on November 12, 2025, in the international journal Advanced Functional Materials.  ※ Paper title: “Implementation of Drug-Induced Rhabdomyolysis and Acute Kidney Injury in Microphysiological System,” DOI: 10.1002/adfm.202513519 This study was supported by the Ministry of Science and ICT and the National Research Foundation of Korea, and more.      

Opening the Door to B Cell-Based Cancer-Rememberin..

  < (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    

Presenting a Brain-Like Next-Generation AI Semicon..

  < (From left) Professor Sanghun Jeon, Ph.D candidate Seungyeob Kim, Postdoctoral researcher Hongrae Cho, Ph.D candidates Sang-ho Lee and Taeseung Jung, and M.S candidate Seonjae Park >     With the advancement of Artificial Intelligence (AI), the importance of ultra-low-power semiconductor technology that integrates sensing, computation, and memory into a single unit is growing. However, conventional structures face challenges such as power loss due to data movement, latency, and limitations in memory reliability. A Korean research team has drawn international academic attention by presenting core technologies for an integrated ‘Sensor–Compute–Store’ AI semiconductor to solve these issues. KAIST announced on December 31st that Professor Sanghun Jeon’s research team from the School of Electrical Engineering presented a total of six papers at the ‘International Electron Devices Meeting (IEEE IEDM 2025)’—the world’s most prestigious semiconductor conference—held in San Francisco from December 8 to 10. Among these, the papers were simultaneously selected as a Highlight Paper and a Top Ranked Student Paper. Highlight Paper: Monolithically Integrated Photodiode–Spiking Circuit for Neuromorphic Vision with In-Sensor Feature Extraction [Link: https://iedm25.mapyourshow.com/8_0/sessions/session-details.cfm?scheduleid=255] Top Ranked Student Paper: A Highly Reliable Ferroelectric NAND Cell with Ultra-thin IGZO Charge Trap Layer; Trap Profile Engineering for Endurance and Retention Improvement [Link: https://iedm25.mapyourshow.com/8_0/sessions/session-details.cfm?scheduleid=124] The research on the M3D integrated neuromorphic vision sensor, selected as a highlight paper, is a semiconductor that stacks the human eye and brain within a single chip. Simply put, the sensors that detect light and the circuits that process signals like a brain are made into very thin layers and stacked vertically in one chip, implementing a structure where the process of 'seeing' and 'judging' occurs simultaneously. Through this, the research team completed the world's first "In-Sensor Spiking Convolution" platform, where AI computation technology that "sees and judges at the same time" takes place directly within the camera sensor. < Figure 1. Summary of research on vertically stacked optical signal-to-spike frequency converter for AI > < Figure 2. Representative diagram of the development of a 2T-2C near-pixel analog computing cell based on oxide thin-film transistors > Previously, this technology required several stages: capturing an image (sensor), converting it to digital (ADC), storing it in memory (DRAM), and then calculating (CNN). However, this new technology eliminates unnecessary data movement as the calculation happens immediately within the sensor. As a result, it has become possible to implement real-time, ultra-low-power Edge AI with significantly reduced power consumption and dramatically improved response speeds. Based on this approach, the research team presented six core technologies at the conference covering all layers of AI semiconductors, from input to storage. They simultaneously created neuromorphic semiconductors that operate like the brain using much less electricity while utilizing existing semiconductor processes, along with next-generation memory optimized for AI. First, on the sensor side, they designed the system so that judgment occurs at the sensor stage rather than having separate components for capturing images and calculating. Consequently, power consumption decreased and response speeds increased compared to the conventional method of taking a photo and sending it to another chip for calculation. < Figure 3. Schematic diagram of a next-generation biomimetic tactile system using neuromorphic devices > < Figure 4. Representative diagram of NC-NAND development research based on Ultra-thin-Mo and Sub-3.5 nm HZO > Furthermore, in the field of memory, they implemented a next-generation NAND flash that uses the same materials but operates at lower voltages, lasts longer, and can store data stably even when the power is turned off. Through this, they presented a foundational technology that satisfies the requirements for high-capacity, high-reliability, and low-power memory necessary for AI.   < Figure 5. Representative diagram of next-generation 3D FeNAND memory development research > < Figure 6. Representative diagram of research on charge behavior characterization and quantitative analysis methodology for next-generation FeNAND memory > Professor Sanghun Jeon, who led the research, stated, "This research is significant in that it demonstrates that the entire hierarchy can be integrated into a single material and process system, moving away from the existing AI semiconductor structure where sensing, computation, and storage were designed separately." He added, "Moving forward, we plan to expand this into a next-generation AI semiconductor platform that encompasses everything from ultra-low-power Edge AI to large-scale AI memory." Meanwhile, this research was conducted with support from basic research projects of the Ministry of Science and ICT and the National Research Foundation of Korea, as well as the Center for Heterogeneous Integration of Extreme-scale & Property Semiconductors (CH³IPS). It was carried out in collaboration with Samsung Electronics, Kyungpook National University, and Hanyang University.      

KAIST Awakens dormant immune cells inside tumors t..

  <(From Left) Professor Ji-Ho Park, Dr. Jun-Hee Han from the Department of Bio and Brain Engineering>   Within tumors in the human body, there are immune cells (macrophages) capable of fighting cancer, but they have been unable to perform their roles properly due to suppression by the tumor. KAIST researchers have overcome this limitation by developing a new therapeutic approach that directly converts immune cells inside tumors into anticancer cell therapies. KAIST (President Kwang Hyung Lee) announced on the 30th that a research team led by Professor Ji-Ho Park of the Department of Bio and Brain Engineering has developed a therapy in which, when a drug is injected directly into a tumor, macrophages already present in the body absorb it, produce CAR (a cancer-recognizing device) proteins on their own, and are converted into anticancer immune cells known as “CAR-macrophages.” Solid tumors—such as gastric, lung, and liver cancers—grow as dense masses, making it difficult for immune cells to infiltrate tumors or maintain their function. As a result, the effectiveness of existing immune cell therapies has been limited. CAR-macrophages, which have recently attracted attention as a next-generation immunotherapy, have the advantage of directly engulfing cancer cells while simultaneously activating surrounding immune cells to amplify anticancer responses. However, conventional CAR-macrophage therapies require immune cells to be extracted from a patient’s blood, followed by cell culture and genetic modification. This process is time-consuming, costly, and has limited feasibility for real-world patient applications. To address this challenge, the research team focused on “tumor-associated macrophages” that are already accumulated around tumors. They developed a strategy to directly reprogram immune cells in the body by loading lipid nanoparticles—designed to be readily absorbed by macrophages—with both mRNA encoding cancer-recognition information and an immunostimulant that activates immune responses. In other words, in this study, CAR-macrophages were created by “directly converting the body’s own macrophages into anticancer cell therapies inside the body.” <Figure . Schematic illustration of the strategy for in vivo CAR-macrophage generation and cancer cell eradication via co-delivery of CAR mRNA and immunostimulants using lipid nanoparticles (LNPs)>

Hemostasis in 1 Second... Boosting Survival Rates ..

  < (From top left) Professor Steve Park, Professor Sangyong Jon, (From bottom left) President Kwang-Hyung Lee, Ph.D canddiate Youngju Son, Ph.D candidate Kyusoon Park >   The leading cause of death due to injuries in war is excessive bleeding. A KAIST research team, in which an Army Major participated, has tackled this issue head-on. By developing a next-generation powder-type hemostatic agent that stops bleeding in one second just by spraying it, they have presented an innovative technology that will change the paradigm of combatant survivability. KAIST announced on December 29th that a joint research team led by Professor Steve Park from the Department of Materials Science and Engineering and Professor Sangyong Jon from the Department of Biological Sciences has developed a powder-type hemostatic agent that forms a powerful hydrogel barrier within approximately one second when sprayed on a wound. This technology reached a high level of perfection as a practical technology considering real combat environments, with an Army Major researcher directly participating in the study. By implementing characteristics that allow instant hardening even under extreme conditions such as combat and disaster sites due to high usability and storage stability, immediate emergency treatment is possible. Until now, patch-type hemostatic agents widely used in medical fields have had limitations in application to deep and complex wounds due to their flat structure, and were sensitive to temperature and humidity, posing limits on storage and operation. Accordingly, the research team developed a next-generation hemostatic agent in powder form that can be freely applied even to deep, large, and irregular wounds. They have secured versatility to respond to various types of wounds with a single powder. < AGCL powder development strategy and fabrication schematin/ Gelation speed and blood absorption capacity of AGCL powder > Existing powder hemostatic agents had limits in hemostatic capability as they functioned by physically absorbing blood to form a barrier. To solve this problem, the research team focused on the ionic reactions within the blood. The ‘AGCL powder’ developed this time has a structure that combines biocompatible natural materials such as Alginate and Gellan Gum (which react with calcium for ultra-fast gelation and physical sealing) and Chitosan (which bonds with blood components to enhance chemical and biological hemostasis). It reacts with cations such as calcium in the blood to turn into a gel state in one second, instantly sealing the wound. Furthermore, by forming a three-dimensional structure inside the powder, it can absorb blood amounting to more than 7 times its own weight (725％). Due to this, it quickly blocks blood flow even in high-pressure and excessive bleeding situations, and showed superior sealing performance compared to commercial hemostatic agents with a high adhesive strength of over '40kPa', a level of pressure that can withstand being pressed strongly by hand. AGCL powder is composed entirely of naturally derived materials, showing a hemolysis rate of less than 3％, a cell viability rate of over 99％, and an antibacterial effect of 99.9％, making it safe even when in contact with blood. In animal experiments, excellent tissue regeneration effects such as rapid wound recovery and promotion of blood vessel and collagen regeneration were confirmed. In surgical liver injury experiments, the amount of bleeding and hemostasis time were significantly reduced compared to commercial hemostatic agents, and liver function recovered to normal levels two weeks after surgery. No abnormal findings were observed in systemic toxicity evaluations. In particular, this hemostatic agent maintains its performance for two years even in room temperature and high humidity environments, possessing the advantage of being ready for immediate use in harsh environments such as military operation sites or disaster areas. Although this research is an advanced new material technology developed with national defense purposes in mind, it has great potential for application throughout emergency medicine, including disaster sites, developing countries, and medically underserved areas. It is evaluated as a representative spin-off case＊ where national defense science and technology expanded to the private sector, as it is capable of everything from emergency treatment on the battlefield to internal surgical hemostasis. ＊Spin-off case: Expanding or transferring national defense science and technology for use in the private sector. Examples include computers, GPS, microwave ovens, etc. < Validation of efficacy in wounds through animal experiments / Validation of efficacy in a liver surgery model > This study was recognized for its scientific innovation and national defense utility simultaneously, winning the 2025 KAIST Q-Day President's Award and the Minister of National Defense Award at the 2024 KAIST-KNDU National Defense Academic Conference. Ph.D candidate Kyusoon Park (Army Major), who participated in the research, stated, “The core of modern warfare is minimizing the loss of human life,” and added, “I started the research with a sense of mission to save even one more soldier.” He continued, “I hope this technology will be used as a life-saving technology in both national defense and private medical fields.” This research, in which KAIST PhD student Kyusoon Park and Ph.D candidate Youngju Son participated as lead authors and was guided by Professor Steve Park and Professor Sangyong Jon, was published online on October 28, 2025, in the international academic journal in the field of chemistry/materials engineering, Advanced Functional Materials (IF 19.0). ※ Paper Title: An Ionic Gelation Powder for Ultrafast Hemostasis and Accelerated Wound Healing, DOI: 10.1002/adfm.202523910 Meanwhile, this research was conducted with the support of the National Research Foundation of Korea (NRF)."      

Turning PC and Mobile Devices into AI Infrastructu..

  < (From left) KAIST School of Electrical Engineering: Dr. Jinwoo Park, M.S candidate Seunggeun Cho, and Professor Dongsu Han >       Until now, AI services based on Large Language Models (LLMs) have mostly relied on expensive data center GPUs. This has resulted in high operational costs and created a significant barrier to entry for utilizing AI technology. A research team at KAIST has developed a technology that reduces reliance on expensive data center GPUs by utilizing affordable, everyday GPUs to provide AI services at a much lower cost. On December 28th, KAIST announced that a research team led by Professor Dongsu Han from the School of Electrical Engineering developed 'SpecEdge,' a new technology that significantly lowers LLM infrastructure costs by utilizing affordable, consumer-grade GPUs widely available outside of data centers. SpecEdge is a system where data center GPUs and "edge GPUs"—found in personal PCs or small servers—collaborate to form an LLM inference infrastructure. By applying this technology, the team successfully reduced the cost per token (the smallest unit of text generated by AI) by approximately 67.6％ compared to methods using only data center GPUs. To achieve this, the research team utilized a method called 'Speculative Decoding.' In this process, a small language model placed on the edge GPU quickly generates a high-probability token sequence (a series of words or word fragments). Then, the large-scale language model in the data center verifies this sequence in batches. During this process, the edge GPU continues to generate words without waiting for the server's response, simultaneously increasing LLM inference speed and infrastructure efficiency. < Figure 1. Language data flow diagram of the developed SpecEdge >  < Figure 2. Detailed computation time reduction method of SpecEdge >  < Figure 3. Illustration of efficient batching of verification requests from multiple edge GPUs on the server GPU within SpecEdge > Compared to performing speculative decoding solely on data center GPUs, SpecEdge improved cost efficiency by 1.91 times and server throughput by 2.22 times. Notably, the technology was confirmed to work seamlessly even under standard internet speeds, meaning it can be immediately applied to real-world services without requiring a specialized network environment. Furthermore, the server is designed to efficiently process verification requests from multiple edge GPUs, allowing it to handle more simultaneous requests without GPU idle time. This has realized an LLM serving infrastructure structure that utilizes data center resources more effectively. This research presents a new possibility for distributing LLM computations—which were previously concentrated in data centers—to the edge, thereby reducing infrastructure costs and increasing accessibility. In the future, as this expands to various edge devices such as smartphones, personal computers, and Neural Processing Units (NPUs), high-quality AI services are expected to become available to a broader range of users. < Figure 4. Conceptual comparison of the developed SpecEdge vs. conventional methods > Professor Dongsu Han, who led the research, stated, "Our goal is to utilize edge resources around the user, beyond the data center, as part of the LLM infrastructure. Through this, we aim to lower AI service costs and create an environment where anyone can utilize high-quality AI." Dr. Jinwoo Park and M.S candidate Seunggeun Cho from KAIST participated in this study. The research results were presented as a 'Spotlight' (top 3.2％ of papers, with a 24.52％ acceptance rate) at the NeurIPS (Neural Information Processing Systems) conference, the world's most prestigious academic conference in the field of AI, held in San Diego from December 2nd to 7th. Paper Title: SpecEdge: Scalable Edge-Assisted Serving Framework for Interactive LLMs Paper Links: NeurIPS Link, arXiv Link This research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) under the project 'Development of 6G System Technology to Support AI-Native Application Services.'      

KAIST Researchers First in the World to Identify S..

  <Photo 1. (From left) Ph.D. candidates Mingyoo Song and Jaehan Kim, Professor Sooel Son, (Top right) Professor Seungwon Shin, Lead Researcher Seung Ho Na>   Most major commercial Large Language Models (LLMs), such as Google’s Gemini, utilize a Mixture-of-Experts (MoE) structure. This architecture enhances efficiency by dynamically selecting and using multiple "small AI models (Expert AIs)" depending on input queries . However, KAIST research team has revealed for the first time in the world that this very structure can actually become a new security threat. A joint research team led by Professor Seungwon Shin (School of Electrical Engineering) and Professor Sooel Son (School of Computing) announced on December 26th that they have identified an attack technique that can seriously compromise the safety of LLMs by exploiting the MoE structure. For this research, they received the Distinguished Paper Award at ACSAC 2025, one of the most prestigious international conferences in the field of information security. ACSAC (Annual Computer Security Applications Conference) is among the most influential international academic conferences in security. This year, only two papers out of all submissions were selected as Distinguished Papers. It is highly unusual for a domestic Korean research team to achieve such a feat in the field of AI security. In this study, the team systematically analyzed the fundamental security vulnerabilities of the MoE structure. In particular, they demonstrated that even if an attacker does not have direct access to the internal structure of a commercial LLM, the entire model can be induced to generate dangerous responses if just one maliciously manipulated "Expert Model" is distributed through open-source channels and integrated into the system. <Figure 1. Conceptual diagram of the attack technology proposed by the research team.> To put it simply: even if there is only one "malicious expert" mixed among normal AI experts, that specific expert may be repeatedly selected for processing harmful queries, causing the overall safety of the AI to collapse. A particularly dangerous factor highlighted was that this process causes almost no degradation in model performance, making the problem extremely difficult to detect in advance. Experimental results showed that the attack technique proposed by the research team could increase the harmful response rate from 0％ to up to 80％. They confirmed that the safety of the entire model significantly deteriorates even if only one out of many experts is "infected." This research is highly significant as it presents the first new security threat that can occur in the rapidly expanding global open-source-based LLM development environment. Simultaneously, it suggests that verifying the "source and safety of individual  expert models" is now essential—not just performance—during the AI model development process. Professors Seungwon Shin and Sooel Son stated, "Through this study, we have empirically confirmed that the MoE structure, which is spreading rapidly for the sake of efficiency, can become a new security threat. This award is a meaningful achievement that recognizes the importance of AI security on an international level." The study involved Ph.D. candidates Jaehan Kim and Mingyoo Song, Dr. Seung Ho Na (currently at Samsung Electronics), Professor Seungwon Shin, and Professor Sooel Son. The results were presented at ACSAC in Hawaii, USA, on December 12, 2025. <Figure 2. Photo of the Distinguished Paper Award certificate> Paper Title: MoEvil: Poisoning Experts to Compromise the Safety of Mixture-of-Experts LLMs Paper File: https://jaehanwork.github.io/files/moevil.pdf GitHub (Open Source): https://github.com/jaehanwork/MoEvil This research was supported by the Korea Internet & Security Agency (KISA) and the Institute of Information & Communications Technology Planning & Evaluation (IITP) under the Ministry of Science and ICT.        

Breakthrough in Intractable Intestinal Disease Tre..

  < (From left) Professor Sung Gap Im (KAIST), Dr. Seonghyeon Park (KAIST), M.S candidate Sang Yu Sun (KAIST), Dr. Mi-Young Son (KRIBB), (Top right) Dr. Tae Geol Lee (KRISS), Dr. Jin Gyeong Son (KRISS) >   Intestinal Stem Cells (ISCs) derived from a patient's own cells have garnered significant attention as a new alternative for treating intractable intestinal diseases due to their low risk of rejection. However, clinical application has been limited by safety and regulatory issues arising from conventional culture methods that rely on animal-derived components (xenogeneic components). A KAIST research team has developed an advanced culture technology that stably grows ISCs without animal components while simultaneously enhancing their migration to damaged tissues and regenerative capabilities. KAIST announced on December 23rd that a joint research team—led by Professor Sung Gap Im from the Department of Chemical and Biomolecular Engineering, Dr. Tae Geol Lee from the Nano-Bio Measurement Group at the Korea Research Institute of Standards and Science and Dr. Mi-Young Son from the Stem Cell Convergence Research Center at the Korea Research Institute of Bioscience and Biotechnology  has developed a polymer-based culture platform that dramatically improves the migration and regeneration of ISCs in a xenogeneic-free environment. To overcome obstacles in the clinical application of stem cell therapies—such as the risk of virus transmission to patients when using substances derived from mouse fibroblasts or Matrigel—the joint research team developed "PLUS" (Polymer-coated Ultra-stable Surface). This polymer-based culture surface technology functions effectively without any animal-derived materials. < Figure 1. Precise control of polymer coating and surface modification via initiated Chemical Vapor Deposition (iCVD) process > PLUS is a synthetic polymer surface coated via a vapor deposition method. By precisely controlling surface energy and chemical composition, it significantly enhances the adhesion and mass-culture efficiency of ISCs. Notably, it maintains identical culture performance even after being stored at room temperature for three years, securing industrial scalability and storage convenience for stem cell therapeutics. Through proteomics analysis＊, the research team identified that the expression of proteins related to cytoskeletal reorganization significantly increased in ISCs cultured on the PLUS environment. Proteomics Analysis: A method used to simultaneously analyze the types and quantitative changes of all proteins present within a cell or tissue. Specifically, the team confirmed that increased expression of cytoskeleton-binding and actin-binding proteins leads to a stable restructuring of the internal cellular architecture. This provides the power source for stem cells to move faster and more actively across the substrate. < Figure 2. Elucidation of the mechanism for enhanced ISC migration through precision proteomics analysis > Real-time observations using holotomography microscopy revealed that ISCs cultured on PLUS exhibited a migration speed approximately twice as fast as those on conventional surfaces. Furthermore, in a damaged tissue model, the cells demonstrated outstanding regenerative performance, repairing more than half of the damage within a single week. This proves that PLUS activates the cytoskeletal activity of stem cells, thereby boosting their practical tissue regeneration capabilities. The newly developed PLUS culture platform is evaluated as a technology that will significantly enhance the safety, mass production, and clinical feasibility of ISCs derived from human pluripotent stem cells (hPSCs). By elucidating the mechanism that simultaneously strengthens the survival, migration, and regeneration of stem cells in a xenogeneic-free environment, the team has established a foundation to fundamentally resolve safety, regulatory, and productivity issues in stem cell therapy. Professor Sung Gap Im of KAIST stated, "This research provides a synthetic culture platform that eliminates the dependence on xenogeneic components—which has hindered the clinical application of stem cell therapies—while maximizing the migration and regenerative capacity of stem cells. It will serve as a catalyst for a paradigm shift in the field of regenerative medicine." Dr. Seonghyeon Park (KAIST), Sang Yu Sun (KAIST), and Dr. Jin Gyeong Son (KRISS) participated as first authors. The research findings were published online on November 26th in Advanced Materials, the leading academic journal in materials science. Paper Title: Tailored Xenogeneic-Free Polymer Surface Promotes Dynamic Migration of Intestinal Stem Cells DOI: 10.1002/adma.202513371 This research was conducted with support from the Ministry of Science and ICT, the Ministry of SMEs and Startups, the National Research Foundation of Korea, the National Council of Science and Technology Research, KRISS, KRIBB, and the National NanoFab Center.    

KAIST, AI judges manufacturing beyond craftsmanshi..

  <(From Left) M.S candidate Inhyo Lee, Ph.D candidate Heekyu Kim, Ph.D candidate joonyoung Kim, Professor Seunghwa Ryu>   Most of the plastic products we use are made through injection molding, a process in which molten plastic is injected into a mold to mass-produce identical items. However, even slight changes in conditions can lead to defects, so the process has long relied on the intuition of highly skilled workers. Now, KAIST researchers have proposed an AI-based solution that autonomously optimizes processes and transfers manufacturing knowledge, addressing concerns that expertise could be lost due to the retirement of skilled workers and the increase in foreign labor. KAIST (President Kwang Hyung Lee) announced on the 22nd of December that a research team led by Professor Seunghwa Ryu from the Department of Mechanical Engineering · InnoCORE PRISM-AI Center has, for the first time in the world, developed generative AI technology that autonomously optimizes injection molding processes, along with an LLM-based knowledge transfer system that makes on-site expertise accessible to anyone. The team also reported that these achievements were published consecutively in an internationally renowned journal. The first achievement is a generative AI–based process inference technology that automatically infers optimal process conditions based on environmental changes or quality requirements. Previously, whenever temperature, humidity, or desired quality levels changed, skilled workers had to rely on trial and error to readjust conditions. The research team implemented a diffusion model–based approach that reverse-engineers process conditions satisfying target quality requirements, using environmental data and process parameters collected over several months from an actual injection molding factory. In addition, the team built a surrogate model that substitutes for actual production, enabling quality prediction without running the real process. As a result, they achieved an error rate of just 1.63％, significantly lower than the 23~44％ error rates of representative existing technologies such as GAN＊ and VAE＊＊ models traditionally used for process prediction. Experiments applying the AI-generated conditions to real processes confirmed successful production of acceptable products, demonstrating practical applicability.  ＊GAN (Generative Adversarial Network): a method in which two AI models compete with each other to generate data  ＊＊VAE (Variational Autoencoder): a method that compresses and learns common patterns in data and then reconstructs them <Figure 1. Generative AI–Based Process Reasoning Technology> The second achievement is the IM-Chat, an LLM-based knowledge transfer system designed to address skilled worker retirement and multilingual work environments. IM-Chat is a multi-agent AI system that combines large language models (LLMs) with retrieval-augmented generation (RAG), serving as an AI assistant for manufacturing sites by providing appropriate solutions to problems encountered by novice or foreign workers. When a worker asks a question in natural language, the AI understands it and, if necessary, automatically calls the generative process inference AI, simultaneously providing optimal process condition calculations along with relevant standards and background explanations. For example, when asked, “What is the appropriate injection pressure when the factory humidity is 43.5％?”, the AI calculates the optimal condition and presents the supporting manual references as well. With support for multilingual interfaces, foreign workers can receive the same level of decision-making support. This research is regarded as a core manufacturing AI transformation (AX) technology that can be extended beyond injection molding to molds, presses, extrusion, 3D printing, batteries, bio-manufacturing, and other industries. In particular, the work is significant in that it presents a paradigm for autonomous manufacturing AI, integrating generative AI and LLM agents through a Tool-Calling approach＊, enabling AI to make its own judgments and invoke necessary functions.  ＊Tool-Calling approach: a method in which AI autonomously calls and uses the functions or programs required for a given situation <Figure 2. Large Language Model–Based Multilingual Knowledge Transfer Multi-Agent IM-Chat> <Figure 3. Example of Operation of the Large Language Model (LLM)–Based Multilingual Knowledge Transfer Multi-Agent IM-Chat> <Figure 4. Illustration of the Application of an LLM-Based Multilingual Knowledge Transfer Multi-Agent IM-Chat (AI-Generated)> Professor Seunghwa Ryu explained, “This is a case where we addressed fundamental problems in manufacturing in a data-driven way by combining AI that autonomously optimizes processes with LLMs that make on-site knowledge accessible to anyone,” adding, “We will continue expanding this approach to various manufacturing processes to accelerate intelligence and autonomy across the industry.” This research involved doctoral candidates Junhyeong Lee, Joon-Young Kim, and Heekyu Kim from the Department of Mechanical Engineering as co–first authors, with Professor Seunghwa Ryu as the corresponding author. The results were published consecutively in the April and December issues of Journal of Manufacturing Systems (JCR 1/69, IF 14.2), the world’s top-ranked international journal in engineering and industrial fields.  ※ Paper 1: “Development of an Injection Molding Production Condition Inference System Based on Diffusion Model,” DOI: https://doi.org/10.1016/j.jmsy.2025.01.008  ※ Paper 2: “IM-Chat: A multi-agent LLM framework integrating tool-calling and diffusion modeling for knowledge transfer in injection molding industry,” DOI: https://doi.org/10.1016/j.jmsy.2025.11.007 This research was supported by the Ministry of Science and ICT, the Ministry of SMEs and Startups, and the Ministry of Trade, Industry and Energy.  

Where did this fish come from？ Securing World-Clas..

  < (From left) KAIST Ph.D. candidate Hyeontaek Hwang, Research Professor Yalew Kidane, Senior Researcher Young-jong Lee, Researcher Geon-woo Park, and (Top) Professor Daeyoung Kim >   When buying seafood at a supermarket, you may have wondered where the fish was caught and what process it went through to reach your dinner table. However, due to complex distribution processes, it has been difficult to transparently track that path. KAIST’s research team has developed a digital technology that solves this problem, allowing the movement path of seafood to be checked at a glance based on international standards recognized worldwide. KAIST announced on December 19th that "OLIOPASS," a GS1 international standard-based digital transformation solution developed by Director Daeyoung Kim (Professor, School of Computing) of the KAIST Auto-ID Labs Busan Innovation Center, has passed the rigorous performance verification of the GDST (Global Dialogue on Seafood Traceability). It is the first in Korea to obtain the "GDST Capable Solution" certification. < (Left) GDST Global Certification Logo, (Right) KAIST OLIOPASS Platform Logo > Only 13 technologies worldwide have received this GDST certification. Among them, only 7 entities, including KAIST, support "Full Chain" traceability technology, which manages the entire process from production and processing to distribution and sales. The GDST is an international organization established in 2015 at the suggestion of the World Economic Forum (WEF). It helps record and share information on all seafood movement processes digitally, according to the GS1 international standard agreed upon by the global community. This can be compared to creating a "common language for the supply chain" used worldwide. The GDST is a global standard system that increases the reliability of seafood history information by defining Key Data Elements (KDEs) that must be recorded during the movement of seafood and Critical Tracking Events (CTEs) that define when, where, and what moved, based on international standards. As major food distribution companies in the United States and Europe have recently begun requiring GDST compliance, this standard is becoming a de facto essential requirement for entering the global market. Since 2019, KAIST has participated as a founding member of GDST and has played a key role in designing seafood traceability models and system-to-system information interoperability. In particular, with the U.S. Food and Drug Administration (FDA) announcing the mandatory enforcement of food traceability (FSMA 204) starting in July 2028, this certification is significant as it secures a technical solution for domestic companies to meet global market regulations. OLIOPASS, which received certification on November 5th, is a digital traceability platform that combines KAIST's IoT technology with international standards (GS1 EPCIS 2.0, GS1 Digital Link). It records and shares movement information of various products and assets in a standardized language and utilizes blockchain technology to fundamentally prevent forgery or alteration. Even if systems differ between companies, history data is seamlessly linked. Furthermore, OLIOPASS is designed as an "AI-ready data" infrastructure, allowing for the easy application of next-generation AI technologies such as Large Multimodal Models (LMM), AI agents, knowledge graphs, and ontologies. This allows it to serve as a platform that supports both digital and AI transformation beyond simple history management. Daeyoung Kim, Director of the KAIST Auto-ID Labs Busan Innovation Center, stated, "This certification is an international recognition of our capability in reliable data technology across the global supply chain. We will expand OLIOPASS beyond seafood and food into various fields such as pharmaceuticals, logistics, defense, and smart cities, ensuring KAIST’s technology grows into a platform used by the world." ※ Related Link: https://thegdst.org/verified-gdst-capable-solutions/ < List of Certified Organizations >