Kaist

KAIST Reconstructs Transparent Structures Through ..

< The research team. From left: Professor Mooseok Jang, PhD candidate Gookho Song, and master > A KAIST research team has developed a technology that reconstructs the shape, optical thickness, and position of a transparent object hidden between two dynamic scattering layers from a single shot. The technology could enable precision inspection of transparent semiconductor and display components, as well as biomedical imaging. KAIST (President Choongsik Bae) announced on August 6 that a research team led by Professor Mooseok Jang from the Department of Bio and Brain Engineering has developed a single-shot phase imaging technique that reconstructs a phase object — a transparent object such as glass, plastic film, or a living cell, which produces almost no visible contrast under an ordinary camera but induces a subtle shift in light called a phase change — from a single measurement, even when the object is fully enclosed between two dynamic scattering layers. < Figure 1. Comparison of measured intensity images through dynamic scattering layers under different illumination conditions. The object is positioned between two dynamic scattering layers, S1 and S2, with S1 in front of the object and S2 behind it. (a) Imaging configuration under plane-wave illumination. (b) Corresponding measurement, in which no object-related diffraction pattern can be identified. (c) Imaging configuration under focused (point) illumination. (d) Corresponding measurement, in which a partially blurred but discernible object-encoded diffraction pattern is observed. > Phase objects are difficult to see with conventional cameras because they show little brightness contrast with their surroundings. However, analyzing the minute phase shift can reveal an object's morphology and optical thickness, and can be used to determine its physical thickness or refractive-index variation when the other quantity is known. For this reason, phase imaging is widely used to observe living cells without staining and to inspect transparent components in semiconductors and displays. The challenge is that when scattering layers positioned in front of and behind an object are in motion — much like the blurred view through a foggy window — the light path continually changes, making it difficult to obtain accurate information about the object. Conventional techniques have therefore required multiple exposures of the same target, prior calibration of the scattering environment, or training an AI model on large volumes of data. To address this, the team tightly focused the illumination onto a small spot on the first scattering layer —much like concentrating light to a point with a magnifying glass— so that the light passing through it would carry the object's information as reliably as possible. < Figure 2. Physical modeling process and proposed reconstruction framework for a dynamic scattering environment. (a) Schematic of the modeling process, in which focused illumination establishes spatial coherence between the two dynamic scattering layers, and the blurred hologram measured behind S2 is expressed as a convolution with a scattering blur kernel. (b) Proposed algorithm that jointly extracts the object > The researchers then combined an optical model, which computes how light changes as it passes through the object and scattering layers, with an AI framework. Rather than training on a large set of reference images as conventional AI approaches do, the framework works backward from physical laws to infer the path the light must have taken to produce the measured pattern. The process is comparable to recovering a clear image from a single blurred photograph taken in fog. Using this approach, the team succeeded in simultaneously determining the shape and thickness of a transparent object, the scattering-induced blur characteristics, and the object's position — all from a single image measuring light intensity. The technology is expected to have applications in a wide range of fields, including precision inspection for semiconductors and displays and biomedical imaging. "This is the first demonstration of restoring the shape and position of a transparent object from a single measurement, even in environments where light is severely scattered, such as behind fog or a diffusive film," said Professor Jang. He added that the team plans to develop the technique further so that it operates reliably in more complex environments, with applications in semiconductor inspection and biomedical imaging. < Figure 3. (a) Experimental configuration including dynamic scattering layers S1 and S2. (b) Measured intensity image of a phase target. (c) Reconstructed phase of the object and the scattering blur kernel. > The study was co-first-authored by Yoosun Kim, a master's student, and Gookho Song, a PhD candidate, both in the KAIST Department of Bio and Brain Engineering, with Professor Jang serving as corresponding author. The paper was published in the international optics journal Optica. Paper title: Single-shot imaging of phase objects fully enclosed by dynamic scattering layers DOI: https://doi.org/10.1364/OPTICA.593328 This research was supported by the National Research Foundation of Korea under the Ministry of Science and ICT (RS-2021-NR060086, RS-2023-00251628, RS-2026-25479811), and by a Samsung Electronics industry–academia strategic project (IO260313-15915-01).

KAIST brings ‘giant batteries’ closer to commercia..

< The research team. From left: Professor Hee-Tak Kim, PhD candidate Kyunghwa Seok (first author), and PhD candidate Minseong Kang (second author) from the KAIST Department of Chemical and Biomolecular Engineering. > The explosive growth of AI data centers has brought the commercialization of "giant batteries" one step closer. A KAIST research team has developed a process that cuts the production time for a core material used in large-capacity batteries by 67％, resolving the largest production bottleneck standing in the way of commercialization. KAIST (President Choongsik Bae) announced on August 5 that a research team led by Professor Hee-Tak Kim from the Department of Chemical and Biomolecular Engineering has developed a process for producing the core electrolyte of vanadium redox flow batteries (VRFBs)—a leading candidate for large-capacity energy storage systems (ESS)—faster and more stably. As AI data centers operate around the clock in growing numbers, large-capacity ESS that can store electricity generated from solar and wind power and supply it reliably when needed have become increasingly important. Because VRFBs use nonflammable, water-based electrolytes, they have a lower fire risk than many conventional battery systems. And their energy-storage capacity can be scaled by increasing the amount of electrolyte stored in external tanks. This has drawn attention to VRFBs as ultra-large batteries suited to AI data centers and renewable energy storage. However, producing the vanadium electrolyte with an average oxidation state of 3.5＋—the standard starting composition for VRFB operation— has been slow and costly, making it a critical obstacle to commercialization. The conventional process first produces the electrolyte through chemical reduction—a reaction in which a chemical reducing agent causes vanadium ions to gain electrons—and then refines it through electrochemical reduction, which applies electric current to adjust the vanadium ions' electron state to the desired level. This final electrochemical step, however, relies on a costly VRFB stack and significant electrical energy, increasing both operational complexity and capital costs.Beyond the limitations of the electrochemical reduction process, the research team found, for the first time, that the alternative chemical reduction process also suffers from a distinct kinetic bottleneck. The reaction rate slows sharply at a specific point, much like highway traffic suddenly backing up at a bottleneck. This bottleneck occurs when the average vanadium oxidation state reaches approximately ＋4.1, an intermediate stage in the production of V3.5＋ electrolyte. In previous research, the team had replaced the conventional electrochemical adjustment step with a Pt/C-catalyzed reduction process, preventing the waste of leftover electrolyte. In the present study, it further extended the catalytic process into the bottleneck region of oxalic-acid-based chemical reduction. By switching from chemical to catalytic reduction at an average oxidation state of approximately ＋4.1, the team was able to bypass the slowest stage of the production process. < Figure 1. Schematic comparison of V3.5＋ vanadium electrolyte production processes. By switching from chemical to catalytic reduction at an average oxidation state of approximately ＋4.1, the redesigned process reduces the total production time to about one-third of that required by the conventional process while preventing catalyst degradation. > As a result, production time for V3.5＋ electrolyte was cut by 67％ compared to the conventional process. The switch also eliminated residual oxalic acid, an impurity that can degrade battery performance. The same catalyst was reused more than 2,500 times without a notable drop in performance, demonstrating the process's viability for industrial-scale production. "This study combined reaction engineering principles with thermodynamic predictions to identify the rate-determining step in the chemical reduction and redesigned the electrolyte production process to overcome this major bottleneck to the commercialization of large-scale batteries," said Hee-Tak Kim, professor in the Department of Chemical and Biomolecular Engineering. He added, "By scientifically identifying the conditions under which the catalyst operates stably without degrading in the electrolyte environment, we resolved a production bottleneck relevant to industry, and we expect this to significantly accelerate the commercialization of large-capacity energy storage technology." Kyunghwa Seok, a PhD candidate in the Department of Chemical and Biomolecular Engineering, led the research as first author. The findings were published online in Advanced Energy Materials—a leading international journal in the energy field—on May 7. In particular, in recognition of its academic significance, the study was selected as the cover article for Issue 34, which is scheduled to be published online in early September. Paper title: Streamlined V3.5＋ Electrolyte Production by Leveraging Chemical and Catalytic Reductions DOI: https://doi.org/10.1002/aenm.71029 Authors: Kyunghwa Seok (KAIST, first author), Minseong Kang (KAIST, second author), and Hee-Tak Kim (KAIST, corresponding author). This research was supported by Lotte Chemical.

KAIST Develops Marine Carbon Removal Technology Th..

< The research team. From left: Professor Dong-Yeun Koh of KAIST’s Department of Chemical and Biomolecular Engineering; Professor T. Alan Hatton of MIT’s Department of Chemical Engineering; doctoral student Inhwan Park (KAIST); and Dr. Young Hun Lee (MIT). > A new pathway has opened to enhance the ocean’s natural ability to clean the planet. KAIST researchers have developed a technology that converts carbon dioxide dissolved in seawater into “stone,” or minerals, preventing it from returning to the atmosphere and enabling permanent storage. The achievement is expected to help the ocean absorb more carbon dioxide and accelerate the commercialization of next-generation marine carbon removal technologies. KAIST (President Choongsik Bae) announced that a research team led by Professor Dong-Yeun Koh from the Department of Chemical and Biomolecular Engineering, in collaboration with Professor T. Alan Hatton’s group at the Massachusetts Institute of Technology (MIT), has developed an electrochemical dissolved ocean carbon removal (e-DOC) technology that converts carbon dioxide dissolved in seawater into calcium carbonate (CaCO₃), a stable mineral form, enabling virtually permanent carbon storage. The ocean is the planet’s largest carbon reservoir, absorbing about 30％ of the carbon dioxide emitted by human activity. Just as water naturally refills a large container when some is removed, removing carbon dioxide from seawater enables the ocean to absorb more carbon dioxide from the atmosphere. < Figure1. Fabrication Process of Stainless-Steel Hollow Fibers and the Hollow Fiber Electrode Assembly (HFEA) > The research team developed a technology that converts dissolved inorganic carbon (DIC), the carbon species dissolved in seawater, into a mineral form that does not return to the atmosphere. Once stored in this form, the carbon is effectively prevented from returning to the air, allowing the ocean to continue absorbing new carbon dioxide. Such technologies are gaining attention as key carbon dioxide removal (CDR) solutions for responding to climate change. However, conventional technologies have faced a major challenge: mineral scaling. Much like limescale building up inside a kettle, minerals such as calcium carbonate adhere to electrode surfaces and clog the system. As operation continues, performance declines, requiring frequent cleaning or replacement of components and increasing both energy consumption and maintenance costs. To overcome this issue, the research team developed a hollow fiber electrode assembly (HFEA), a device composed of bundled hollow, thread-like electrodes. In this structure, minerals form outside the electrode surface rather than directly on it, while hydrogen bubbles naturally generated during the reaction act like a brush, continuously cleaning the electrode surface and preventing mineral buildup. In experiments using Jeju lava seawater, the team successfully operated the device continuously and stably for more than 120 hours. The system removed 80–90％ of dissolved inorganic carbon from seawater and reduced electricity consumption by up to 54％ compared with existing technologies. In addition, the process simultaneously produced high-purity hydrogen (H₂) and magnesium hydroxide (Mg(OH)₂), a material used in eco-friendly products and industrial applications, further improving its economic potential. < Figure 2. Schematic Illustration of the Electrochemical Reactions in the HFEA > The newly developed device can be produced in a compact, modular form, making it suitable for installation on ships, offshore plants, and other marine industrial facilities. The research team expects the technology to be scaled up into large-scale marine carbon removal systems that can contribute to achieving carbon neutrality and responding to climate change. Professor Dong-Yeun Koh said, “This technology converts carbon dioxide dissolved in seawater into a mineral form that does not return to the atmosphere, enabling permanent storage and helping the ocean continuously absorb new carbon dioxide,” adding, “We expect this work to accelerate the commercialization of marine carbon removal technologies and contribute to the realization of a carbon-neutral society.” This study was co-led by KAIST Ph.D. candidate Inhwan Park of the Department of Chemical and Biomolecular Engineering and Dr. Young Hun Lee of MIT, who received his Ph.D. from KAIST in 2023 and is currently affiliated with the Department of Chemical Engineering at MIT, as co-first authors. The paper was published online on June 19, 2026, in the international journal Advanced Energy Materials. Paper title: A Compact Hollow Fiber Electrode Assembly Architecture for Continuous Electrochemical Marine Carbon Dioxide Removal DOI: https://doi.org/10.1002/aenm.71205 This research was supported by Hyundai Motor Company and Kia, as well as the Global C.L.E.A.N. Program of the National Research Foundation of Korea funded by the Ministry of Science and ICT.

KAIST Develops AI That Generates Feasible Plans fo..

< The research team. From left: Professor Min-Soo Kim (corresponding author); Tae-Hoon Lee, a doctoral student > From parcel delivery routes and factory production schedules to hospital duty rosters, many real-world planning tasks require solutions that satisfy numerous operational constraints. KAIST researchers have developed an artificial intelligence technique that can independently generate feasible plans satisfying all constraints specified in a mathematical optimization problem. KAIST (President Choongsik Bae) announced on August 3 that a research team led by Professor Min-Soo Kim from the School of Computing has developed RL-SPH (Reinforcement Learning-based Start Primal Heuristic), a reinforcement learning technique that trains AI to independently produce feasible plans without relying on an external solver. The key feature of the technology is its ability to learn how to produce solutions that satisfy the multiple constraints encoded in an optimization problem. The research team expects the method to serve as an important foundation for AI-based decision-making in fields including logistics, manufacturing, semiconductor production, and workforce management. Parcel delivery routing, vehicle routing, factory production scheduling, and hospital staff rostering are representative planning problems that can be formulated using integer linear programming, or ILP. ILP is a mathematical optimization technique for finding the most efficient solution while satisfying a set of linear constraints and requiring some or all decision variables to take integer values. A parcel delivery plan, for example, must do more than simply minimize delivery time. It must also comply with vehicle capacity limits and driver working-hour requirements while ensuring that every destination is visited. A route that violates even one of these conditions cannot be used in practice, regardless of how short or inexpensive it may appear. < Figure 1. Comparison among end-to-end learning-based primal heuristics (E2EPH), start primal heuristics (SPH), and ours. > Existing learning-based approaches can rapidly generate approximate or partial solutions, but these predictions frequently violate constraints. Consequently, many approaches pass their outputs to specialized ILP solvers, such as Gurobi or SCIP, which are then responsible for obtaining a feasible solution. The paper notes that existing end-to-end learning-based primal heuristics generally struggle to generate feasible solutions independently. RL-SPH addresses this limitation by iteratively revising a candidate solution rather than attempting to predict the final answer in a single step. At each stage, it selects multiple decision variables that are likely to improve feasibility and determines whether their values should be increased, decreased, or left unchanged. The model then learns from the resulting changes in constraint violations and solution quality. Notably, the team designed the AI to first find a plan that is actually usable, rather than the single best plan. The overall procedure consists of two stages. In the first stage, the AI prioritizes finding an initial feasible solution that satisfies all constraints. In the second stage, it seeks a higher-quality solution by reducing the objective value, such as cost or processing time, while maintaining feasibility. For example, in a factory production-planning problem, the method would first identify a schedule that satisfies requirements such as delivery deadlines, equipment capacity, and available labor. It would then attempt to reduce production cost and time without violating those conditions. The research therefore prioritizes finding a plan that can actually be implemented before attempting to optimize it further. The team also introduced ILP-GT, a new AI model that learns the relationships between variables and constraints, along with a feasibility-aware search strategy that prioritizes revising the variables most effective for resolving the problem, substantially improving computational efficiency. Across five representative benchmarks, RL-SPH achieved a 100％ feasibility rate, successfully finding a usable plan for every problem. It maintained the same performance even on more complex problems involving general (non-binary) integer variables. < Figure 2. The overview of RL-SPH. > Compared with existing techniques, RL-SPH reduced the primal gap — the gap between a method's solution and the best-known solution — by an average of 28.6 times, and improved the primal integral — a measure of the speed and quality of the search process — by 2.6 times. The time needed to find the first feasible plan was also 2.5 times faster on average. Among recent AI techniques such as PAS, DDIM, and DiffILO, RL-SPH was the only method to achieve a 100％ feasibility rate across three benchmarks compared (SC, CA, IS). Its training also took an average of just 30 minutes — 14.7 times faster than existing techniques and roughly 34 times faster than the most recent unsupervised learning — an AI training method that finds patterns in data without being given the correct answers in advance — based technique. The technique further demonstrated its generalization potential on MIPLIB, an international benchmark library for mixed-integer programming widely used in academia and industry. It reliably found feasible plans not only for problems up to 67 times larger than those it was trained on, but also for entirely new problem types it had never encountered during training. “In real-world applications, a plan that can actually be implemented is often more important than a theoretically optimal answer that violates practical constraints,” said Professor Kim. He added, “This research demonstrates that AI can learn to generate feasible solutions without relying on a specialized optimization solver to enforce feasibility. We expect the technology to provide an important foundation for AI-based decision-making in logistics, manufacturing, semiconductor production, workforce management, and other industrial fields.” Tae-Hoon Lee, a doctoral student in the KAIST School of Computing, participated as the first author, and Professor Min-Soo Kim led the research. The findings were presented at the 43rd International Conference on Machine Learning, or ICML 2026, held in Seoul from July 6 to 11. ICML is regarded as one of the world’s premier international conferences in machine learning. Paper title: RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programs DOI: https://doi.org/10.48550/arXiv.2411.19517 Authors: Tae-Hoon Lee (KAIST, first author), Min-Soo Kim (KAIST, corresponding author) This research was supported by the Ministry of Science and ICT and the Institute of Information & Communications Technology Planning & Evaluation through related software research and Information Technology Research Center programs, as well as by the National Research Foundation of Korea. The paper’s acknowledgements specifically identify NRF and IITP support, including an ITRC grant.

KAIST Develops AI That Avoids Hallucinating Even a..

< The research team. From left: Sangyun Chung (KAIST, first author of the MAD study and co-first author of the DNA study); Yong Man Ro (KAIST, corresponding author); Youngjoon Yu (KAIST, co-first author of the DNA study) > Multimodal large language models (MLLMs), which process multiple types of sensory information such as text, images, and audio at the same time, are rapidly expanding the range of applications for artificial intelligence (AI). However, in real-world environments, these models can misinterpret the physical characteristics of sensors, mistakenly identify objects, or claim to hear sounds that are not actually present simply because a certain object appears in a video. These errors are known as hallucinations. A KAIST research team has developed a new technology that corrects such information confusion and physical misperceptions in AI. KAIST (President Choongsik Bae) announced on the 31st of July that a research team led by Professor Yong Man Ro from the School of Electrical Engineering has developed two core technologies that overcome the tendency of existing large language models to rely too heavily on ordinary camera (RGB) images and enable AI to suppress cross-modal hallucinations that occur when different sensory inputs become mixed. < Figure 1. Examples of vision sensor-related questions and responses by recent. It fails to understand the core principle of thermal imaging, incorrectly attributing brightness to reflected sunlight rather than emitted heat. (Courtesy of KAIST) > The first technology developed by the research team is the Diverse Negative Attributes (DNA) optimization method, which helps AI accurately understand the physical characteristics of special camera sensors such as thermal, depth, and X-ray sensors. Existing AI models often failed to understand the physical meaning of such images, for example by mistaking bright areas in thermal images for simple light reflection. The research team built VS-TDX, the first comprehensive benchmark for evaluating diverse vision sensors, and used the types of wrong answers that AI frequently produces as learning signals to help the model internalize the characteristics of each sensor. As a result, the AI gained a “new eye” that allows it to accurately infer the state of objects even in darkness or smoke. The second technology is Modality-Adaptive Decoding (MAD), a control method that blocks hallucinations caused by confusion between visual and auditory information at the source. This technology prevents AI from mistakenly claiming that it hears a sound that does not actually exist simply because a certain object appears in a video. MAD works by having the AI self-assess whether vision or audio is more important for a given task, and then increasing the weight of the more relevant modality in real time. A key advantage of this technology is that it can immediately suppress hallucination errors without costly model retraining, as it is training-free. Instead of retraining AI models at large scale with massive computing resources, the research team maximized cost efficiency by introducing the DNA method, which enables fine adjustment with only a small amount of data, and the MAD plug-in approach, which requires no additional training at all. < Figure 2. Cross-modal hallucinations and their mitigation through Modality-Adaptive Decoding (MAD). Influenced by the boat shown in the video, the base model hallucinates non-existent visual content and audio events—including the splash of a fish jumping out of the water—shown in red and blue text. MAD adaptively suppresses cross-modal interference, producing an accurate description grounded in the actual visual and audio content, shown in green text. (Courtesy of KAIST) > These technologies can be applied to autonomous vehicles operating at night or in bad weather, robots performing missions in smoke-filled environments, and unmanned aerial vehicles using thermal cameras. They are also expected to be useful in fields that process multiple types of sensor information together, such as airport X-ray security screening and medical image analysis. Professor Yong Man Ro said, “This research is significant because it reduces AI’s sensory bias and misperceptions without large-scale retraining,” adding, “It will serve as a foundation for building multimodal AI that can be trusted in real-life and industrial settings.” This achievement was notable for its continuity, with Sangyun Chung, a doctoral student in KAIST’s School of Electrical Engineering, participating as first author in both studies. Dr. Youngjun Yoo also participated as co-first author in the DNA study. Among the related papers, the MAD study was presented in June at the Conference on Computer Vision and Pattern Recognition (CVPR), the world’s leading international conference in AI and computer vision. The DNA study was published in IEEE Transactions on Image Processing, a leading international journal in the field of image processing. Paper title: Enhanced Vision-Language Models for Diverse Sensor Understanding: Cost-Efficient Optimization and Benchmarking, DOI: 10.48550/arXiv.2412.20750 Author information: Sangyun Chung (KAIST, co-first author), Youngjun Yoo (KAIST, co-first author), Se Yeon Kim (KAIST, third author), Youngchae Chee (KAIST, fourth author), Yong Man Ro (KAIST, corresponding author) Paper title: MAD: Modality-Adaptive Decoding for Mitigating Cross-Modal Hallucinations in Multimodal Large Language Models, DOI: 10.48550/arXiv.2601.21181 Author information: Sangyun Chung (KAIST, first author), Se Yeon Kim (KAIST, second author), Youngchae Chee (KAIST, third author), Yong Man Ro (KAIST, corresponding author) Related demo video: https://youtu.be/VuP9i6Vfk8o This research was supported by the Institute of Information & Communications Technology Planning & Evaluation’s (IITP’s) Human-Centered AI Core Technology Development Program and by a Center for Applied Research in Artificial Intelligence (CARAI) grant funded by the Defense Acquisition Program Administration (DAPA) and the Agency for Defense Development (ADD).

KAIST Develops AI That Finds Its Own Hidden Weakne..

< The research team. From left: Professor Junmo Kim; Ph.D. candidate, Minchan Kwon > KAIST researchers have developed a safety verification technology that uncovers roughly seven times more hidden vulnerabilities in AI than existing methods. The technology is expected to serve as a foundation for developing safer, more trustworthy AI. KAIST (President Choongsik Bae) announced on the 30th of July that a research team led by Professor Junmo Kim from the School of Electrical Engineering has developed a new framework called Stable-GFlowNet (S-GFN), which overcomes the limitations of red-teaming—a safety verification process that deliberately attacks large language models (LLMs) to expose hidden weaknesses. < Figure 1. Overall schematic of the S-GFN method. This figure illustrates the training of the Attacker LLM, which generates attack prompts. The Victim LLM produces responses to these prompts, and the Toxic Classifier scores the responses. The resulting reward is used for gradient calculation. The diagram showcases all three core components of S-GFN: the Min-K Fluency Stabilizer, Noisy Gradient Pruning, and Contrastive Trajectory Balance. > Red-teaming for generative AI is the process of crafting attack prompts designed to probe an AI's vulnerabilities and induce the AI to produce harmful or dangerous responses before the program is deployed. Since discovering a wider variety of attack methods allows more vulnerabilities to be addressed in advance, both the success rate and diversity of attacks are critical. Previous approaches primarily relied on reinforcement learning—an AI technique trained to maximize reward—to generate attack prompts. However, these methods frequently suffered from mode collapse—a phenomenon where the model repeatedly converges on a narrow set of high-reward attack prompts rather than generating diverse outputs, thereby limiting its ability to uncover various vulnerabilities. Generative Flow Networks (GFlowNets)—an AI generation technique trained to produce diverse outputs in proportion to their reward—were proposed as a solution. Yet GFlowNet training is computationally complex and unstable, and noisy reward signals can assign high rewards even to meaningless sentences, often causing training to collapse. < Figure 2. Comparison between S-GFN and existing methods. S-GFN effectively bypasses the estimation of the partition function Z through pairwise comparison, thereby achieving the stability that was previously lacking in GFN-based approaches. > To address these issues, the research team developed three core techniques that help the model learn effective attacks more reliably while filtering out flawed ones. First, much like comparing several paths to choose the best one, the team introduced Contrastive Trajectory Balance (CTB), which reduces computational complexity and stabilizes training by directly comparing pairs of generated attack trajectories. Second, akin to filtering out background noise to focus on a single voice, the team applied Noise Gradient Pruning (NGP) to eliminate minor reward fluctuations and ensure the model learns exclusively from meaningful signals. Third, the team applied the Min-K Fluency Stabilizer (MKS), which guides the model to generate attack prompts resembling text that a real user would write—just as a human reader naturally prefers coherent sentences to gibberish. As a result, Stable-GFlowNet discovered 134 unique attack types—about seven times more than the 17 unique attack types found by the existing GFlowNet-based technique—while maintaining a high attack success rate of 92％. Defense models trained using attacks generated by Stable-GFlowNet also demonstrated strong generalization, effectively defending against a wide range of attacks in cross-attack tests, which evaluate performance using attack techniques different from those used during training. The team further demonstrated that CTB and NGP achieve faster and more stable performance than existing methods—not only in AI safety verification, but also in other distribution-matching tasks such as molecular generation for drug discovery. < Figure 3. Graph comparing the Attack Success Rate (ASR) and attack diversity (# of unique attacks) between S-GFN and existing methods. Stable-GFN increases attack diversity by a factor of seven compared to standard GFNs, all while maintaining an ASR of over 90％. > Professor Kim said, "This technology is significant in that it can reliably uncover a wide range of AI vulnerabilities even in realistic conditions with limited data and high noise." He added, "Because it allows a broader range of risk factors to be identified and defended against before generative AI is deployed in real-world services, we expect it to become a core foundational technology for developing safer, more trustworthy AI." The study was led by first author Minchan Kwon, a Ph.D. candidate from the School of Electrical Engineering, and was selected as a Spotlight paper—placing it in the top 2.2％ of submissions—at the International Conference on Machine Learning (ICML) 2026, one of the world's most prestigious AI conferences. ※ Paper title: Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance arXiv: https://arxiv.org/abs/2605.00553 This research was supported by the Institute of Information & Communications Technology Planning & Evaluation’s (IITP) SW Star Lab program, funded by the Ministry of Science and ICT.

KAIST Finds Algal Blooms Make Plastic More Prone t..

< The research team. From left: Youngju Kim, a doctoral student from the Department of Civil and Environmental Engineering (first author); Professor Jaewook Myung (corresponding author) > Every summer, algal blooms turn rivers and lakes green. Although they are widely known as a major form of water pollution that makes the water murky, a KAIST research team has now shown for the first time that algal bloom conditions can make discarded plastics, such as plastic bags, more prone to breaking apart, potentially accelerating the formation of microplastics. The study points to a new direction for the era of climate change: water pollution and plastic pollution need to be managed together, rather than as separate problems. KAIST (President Choongsik Bae) announced on July 29 that a research team led by Professor Jaewook Myung from the Department of Civil and Department of Civil and Environmental Engineering has found, through a microcosm experiment using water collected from Duck Pond, a pond on the KAIST campus, that algal blooms alter the microbial ecosystem on the surface of low-density polyethylene (LDPE) — a common plastic used in plastic bags — and accelerate its early-stage weathering, in which the surface oxidizes and develops microscopic cracks. The study is significant because it suggests that, in natural environments, plastic pollution and the eutrophication that drives algal blooms can interact and lead to new ecological changes. Over time, plastic debris discarded in rivers and lakes becomes colonized by a wide variety of microorganisms, creating a small ecosystem of its own on the plastic surface. This ecosystem is known as the “plastisphere.” The plastisphere is known to influence the spread of pathogens and the transport of microplastics, but little has been known about how algal blooms, a serious form of water pollution, affect this microbial ecosystem. To investigate this question, the research team constructed microcosms — small-scale experimental systems that recreate natural environments in the laboratory — in which algal blooms were artificially induced by controlling light exposure and nutrient concentrations. Over the following six weeks, the researchers closely analyzed the biofilms forming on the plastic surface, the succession of microbial communities, and changes in their functional gene profiles. The analysis showed that under eutrophic conditions in which excessive nutrients trigger algal blooms that cyanobacteria, a major group of photosynthetic bacteria, proliferated alongside a variety of other bacteria, forming a thicker biofilm on the plastic surface. Microorganisms capable of producing large amounts of extracellular polymeric substances (EPS) also became significantly more abundant. EPS is a sticky, mucilage-like material that binds microorganisms together and helps them adhere to plastic surfaces. < Figure 1. (Top) Water sampling at KAIST’s Duck Pond and microcosms constructed using the collected water samples. (Bottom) The microbial biofilm formed on the plastic surface under eutrophic conditions over 42 days. > As the microbial ecosystem on the plastic surface changed, the early weathering of the plastic — including surface oxidation and the formation of microscopic cracks — also accelerated. The team found that microorganisms harboring genes encoding enzymes associated with plastic oxidation and early-stage degradation became more abundant under eutrophic conditions. Analyses using Fourier-transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM) directly confirmed these changes. Oxygen-containing functional groups associated with oxidation, including carbonyl and hydroxyl groups, increased on the plastic surface, while more fine, hairline cracks appeared. These changes indicate that the plastic had become more susceptible to further physical weathering and fragmentation. The results suggest that these changes were driven not by a single microbial species, but by the combined activity of a microbial ecosystem comprising photosynthetic and other bacteria. Professor Myung said, “As algal blooms become more frequent because of climate change, we expect this work to provide an important scientific basis for integrated environmental management strategies that consider water quality management and plastic waste management together.” The study was led by first author Youngju Kim, a doctoral student from the Department of Civil and Environmental Engineering, and was published online in the international environmental journal Water Research on May 25, 2026. Paper title: Eutrophication drives taxonomic and functional trajectories in plastic-associated biofilms DOI: 10.1016/j.watres.2026.126183 Author information: Youngju Kim, KAIST, first author; Yijin Wang, HKUST; Wenqian Xu, HKUST; Charmaine C.M. Yung, HKUST; and Jaewook Myung, KAIST, corresponding author — five authors in total This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS- 2023–00209472, RS-2024–00437656, and RS-2026–25393325), by the Ministry of Oceans and Fisheries, Korea (20200104), and by the grant for the “KAIST Grand Challenge 30 Program” funded by the Korea Advanced Institute of Science and Technology (N11250072)

KAIST Professor Sooel Son Selected for Microsoft F..

< Professor Sooel Son, School of Computing > KAIST (President Choongsik Bae) announced on the 28th of July that Professor Sooel Son has been selected as the only researcher in Korea to receive funding from Microsoft for research on artificial intelligence safety and security. The funding was awarded through the External Red Team Alliance (EXTRA), a new program established by Microsoft’s AI Red Team, which examines the safety and security vulnerabilities of AI systems. EXTRA is a global initiative designed to strengthen AI safety and security research capabilities by supporting researchers at universities and technology experts around the world. Microsoft noted that most AI safety testing is still conducted internally by individual companies or organizations. However, assessing the major risks posed by increasingly advanced AI systems requires expertise across a broad range of fields, including cybersecurity, multilingual environments, regional and cultural contexts, AI alignment, and potential misuse. EXTRA was launched in recognition of the difficulty a single internal organization faces when comprehensively evaluating these diverse risks. Through the program, Microsoft will provide KAIST with an unrestricted gift of USD 25,000, approximately KRW 37 million, with no prescribed project period, to support research related to AI safety, security, alignment, and responsible AI development. The funding will be used to support Professor Son’s research team in its work on AI security and safety. More than a dozen universities across six continents are participating in EXTRA, with KAIST being the only university selected from Korea. Through the program, Microsoft plans to expand the ecosystem for independent AI safety research and strengthen collaboration between academia and industry. Professor Son’s research team has been conducting research on the security and privacy of AI systems that use machine-learning models and large language models. In particular, the team analyzes adversarial attacks against deep neural networks and language models—including model extraction, membership inference, personal information extraction, model inversion, machine unlearning, and prompt injection—and develops defense methodologies to assess and improve model safety. Building on these technologies, the team is also focusing on establishing systematic defense methodologies that enable the safe and trustworthy deployment of agentic AI systems operating in real-world service environments, including web agents and agentic browsers. “As AI systems rapidly spread throughout society, research that verifies the security and reliability of increasingly advanced AI is becoming more important,” said Professor Son. “Our participation in Microsoft AI Red Team’s EXTRA program will provide an opportunity to further advance our research on the safe development and use of AI systems.” “AI safety research has never been more important, and universities have a critical role to play in advancing the field,” said Ram Shankar Siva Kumar, who leads the Microsoft AI Red Team. “Through EXTRA, we aim to support researchers working to deepen our understanding of how increasingly powerful AI systems can be evaluated, protected, and governed responsibly.” “Competition in AI technology is expanding beyond performance to encompass safety and trustworthiness,” said KAIST President Choongsik Bae. “KAIST’s participation as the only Korean research institution in Microsoft’s global AI safety research network is a meaningful achievement that demonstrates Korea’s competitiveness in AI research. We will continue to lead the advancement of responsible AI technologies that everyone can trust and use by pursuing world-class research in AI safety and security.”

KAIST Makes Cancer Cells Send Out Their Own Danger..

< Prof. Yeu-Chun Kim (KAIST), Prof. Yong-Kyu Lee (Korea National University of Transportation), Dr. A-Reum Yoon (Hanyang University), and Dr. Soo-Sam Lee (KAIST) > Cancer cells survive by hiding from the immune system's surveillance. A KAIST research team has developed a new anticancer platform that makes cancer cells send out their own danger signal—prompting immune cells to attack—while simultaneously delivering gene therapy. The approach is expected to offer a new treatment strategy that combines cancer immunotherapy and gene therapy in a single nanoparticle. Immunogenic cell death (ICD) is a process in which dying cancer cells send danger signals to nearby immune cells, prompting them to attack. A polypeptide is a polymer made of a long chain of amino acids. KAIST (President Choongsik Bae) announced on 28th of July that a team led by Professor Yeu-Chun Kim from the Department of Chemical and Biomolecular Engineering has developed a "helical polypeptide nanoparticle" platform that induces severe stress inside cancer cells to trigger immunogenic cell death, while also delivering a range of gene therapeutics into the cells. The body's immune cells effectively eliminate external invaders such as viruses and bacteria, but cancer cells evade immune surveillance through a variety of immune-escape strategies. This failure of immune cells to recognize cancer cells as a threat has long been one of the biggest limitations in cancer treatment. Recently, researchers have been actively exploring the use of nanoparticles to deliver drugs and genes to cancer cells and activate immune responses. However, it has not been clearly established which properties of nanomaterials actually induce cellular stress and activate antitumor immune responses. By comparing and analyzing a range of nanoparticles, the team confirmed that not just the chemical composition, but the helical, coiled shape of the nanomaterial is a key factor determining therapeutic efficacy. In particular, when a positively charged quaternary amine—a chemical structure that binds readily to cell membranes—was combined with a helical structure, the particle could penetrate the cell membrane like a screw and enter cancer cells with ease. By contrast, particles with the same chemical composition but lacking the helical coil barely entered cells at all and failed to induce an immune response. The helical nanoparticles developed by the team preferentially seek out and penetrate cancer cells, which have different membrane electrical properties from normal cells. Once inside, the particles disrupt the membranes of mitochondria—the cell's energy-producing organelles—and other organelles, subjecting the cancer cell to severe stress. Under this extreme stress, the dying cancer cell releases damage-associated molecular patterns (DAMPs)—distress signals indicating "a dangerous cell is here"—into the surrounding environment. Immune cells that detect these signals recognize the previously hidden cancer cell as a threat and begin their attack. In effect, the cancer cell is made to broadcast its own location to the immune system. The nanoparticle does more than trigger an immune response—it also functions as a carrier for gene therapeutics. Messenger RNA (mRNA), which carries the genetic information for protein synthesis, and small interfering RNA (siRNA), which suppresses the expression of specific genes, are both typically difficult to deliver into cells. The team's nanoparticles, however, delivered these molecules effectively into the cytoplasm. The researchers also introduced guanidinium, a chemical functional group that binds strongly to genetic material, at an optimized ratio, enabling the particles to remain stable in the bloodstream while delivering gene therapeutics effectively. In mouse models of melanoma and colorectal cancer, the team loaded the helical nanoparticles with siRNA targeting PD-L1 (Programmed Death-Ligand 1), an immune-evasion protein, and administered them. Tumor growth was suppressed by approximately 70–80％, and a marked increase was observed in cytotoxic T cells—which directly attack cancer cells—infiltrating the tumor, indicating a substantial boost in antitumor immune response. "This study presents a new anticancer platform in which the nanomaterial does more than simply deliver a therapeutic agent—it drives cancer cells to trigger their own immune response," said Professor Yeu-Chun Kim. He added that the platform is expected to contribute to the development of next-generation treatments combining cancer immunotherapy and gene therapy. Dr. Susam Lee, the paper's first author, added, "We showed that it is not just the composition of the nanomaterial but the helical structure itself that is the key factor determining therapeutic efficacy." He said he hopes the findings will serve as a new benchmark for designing next-generation immuno-oncology nanomaterials. The study was published online in Biomaterials, a leading international journal in the field of biomaterials, on May 28, 2026. Paper title: Helical quaternary amine polypeptide programs membrane stress to drive immunogenic cell death and cytosolic gene delivery for cancer immunotherapy DOI: 10.1016/j.biomaterials.2026.124337 This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT), the Biomedical Global Talent Nurturing Program of the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (RS-2025-25459605 to Susam Lee) and the Korea Basic Science Institute (National research Facilities and Equipment Center).

KAIST Develops AI That Learns to Theorize the Worl..

< The research team. From left: Sungjin Ahn, Professor (KAIST School of Electrical Engineering and Computer Science & School of Computing); Doojin Baek, Master > A KAIST research team has developed a next-generation world model, an internal model an AI builds to understand and predict the world, that learns executable theories from observation alone. KAIST (President Choongsik Bae) announced on the 15th of July that a team led by Professor Sungjin Ahn from the School of Computing has proposed a new learning paradigm called Learning-to-Theorize (L2T), which trains AI to theorize how the world works using only observed information. The team also built the Neural Theorizer (NEO), a neural network-based model that implements this paradigm. The research was selected for an oral presentation at the 43rd International Conference on Machine Learning (ICML 2026), held in Seoul from July 6 to 11, and was presented on July 9. This places it among the top 0.7 percent (168 papers) of the 23,918 total submissions. The paper was also selected for the Best Paper Award at the Compositional Learning Workshop. World models are a foundational technology across robot control, autonomous driving, generative AI, and autonomous agents — AI systems capable of judging and acting on their own. Until now, world models have mainly focused on predicting what happens next. Even when a model predicts the next scene accurately, it doesn't necessarily understand why that change occurred — that is, the underlying principle governing the world. < The award ceremony at the Compositional Learning Workshop, ICML 2026. NEO was selected and honored as the Best Paper Award. > The team found a solution in how humans learn. Long before children acquire language, they build their own internal theories of how the world works. Applying this view from developmental cognitive science to AI, the researchers built a system that understands the principles behind the world, rather than one that simply predicts the future. The team's proposed L2T framework provides no predetermined answers or rules. Given only a "before" and "after" observation, the AI discovers on its own which rule produced the change. While conventional AI models focus on "guessing what comes next," this approach is built to understand "why the change happened." To implement this, the team also developed Neural Theorizier, NEO. The model discovers reusable primitives hidden within observed transformations and composes them into executable programs. These learned primitives can then be systematically recombined to explain new situations. < Figure 1. Learning to Theorize (L2T) Framework. (a) Training data consists of observation pairs (x, y) generated by unobserved true programs. (b) Under L2T, the model learns to discover reusable primitives (Rotate, Left, Down, and Paint) and to compose them into executable theories. (c) Without L2T, the model instead memorizes entangled composite primitives (e.g., Left-Down) as indecomposed single units. (d) Once the model has learned to theorize, novel phenomena (e.g., Down-Paint-Rotate) can be explained by recombining learned primitives. (e) In contrast, memorized entangled representations fail to generalize to unseen programs. > For example, NEO independently learns primitives corresponding to basic operations such as rotation, movement to the left or downward, and coloring. Even when presented with a combination it has never encountered during training, such as “move down, then color, then rotate”, it can recombine the primitives it has already learned to explain and solve the new situation. Conventional AI, by contrast, tends to memorize entangled patterns as a single unit, so its performance drops sharply when faced with an unfamiliar combination. Through a range of experiments, the team demonstrated that NEO outperforms existing approaches in compositional generalization, the ability to combine learned basic rules to solve problems never seen before. "It points to a new direction beyond prediction-centric world models, what we call a 'World Theory Model.' We expect this to develop into a core technology across fields including intelligent robots, autonomous agents, and AI that supports scientific discovery." said Professor Sungjin Ahn. Master's students Doojin Baek and Gyubin Lee from the School of Computing served as co-first authors on the study. Paper title: Learning to Theorize the World from Observation. DOI: https://doi.org/10.48550/arXiv.2605.03413 Authors: Doojin Baek＊, Gyubin Lee＊, Junyeob Baek, Hosung Lee, Sungjin Ahn (＊Co-first authors) The research was supported by the National Research Foundation of Korea (NRF).

KAIST Develops Low-Temperature Technique for Growi..

< The research team. From left: Gayeon Lee, Ph.D. student; Mingyu Jang, student in the integrated M.S.–Ph.D. program; Changhwan Kim, student in the integrated M.S.–Ph.D. program; Professor Joonki Suh; Namwook Hur, student in the integrated M.S.–Ph.D. program; and Dr. Wenxuan Zhu, postdoctoral researcher. > Building next-generation semiconductors and low-power electronic devices requires precisely stacking materials with different functions. In this process, it is essential to preserve each material's intrinsic properties, as well as the interface where the two materials meet, without damage. Layered van der Waals materials, including transition metal dichalcogenides (TMDs), have attracted considerable attention as next-generation semiconductor platforms because their layers interact through weak forces, enabling different materials to be stacked while maintaining atomically clean interfaces. KAIST (President Choongsik Bae) announced on the 27th of July that a research team led by Professor Joonki Suh from the Department of Chemical and Biomolecular Engineering, in collaboration with Professor Bonggeun Shong's team at Hanyang University and Professor Yimo Han's team at Rice University in the United States, has developed a new semiconductor manufacturing technique based on atomic layer deposition (ALD). ALD is a thin-film deposition process in which semiconductor precursor are supplied sequentially, enabling uniform thin films to be deposited with atomic-level control over their thickness. The research team focused on van der Waals materials. These two-dimensional semiconductor materials consist of multiple atomic layers held together by weak interlayer forces, allowing them to be peeled apart into sheets as thin as paper. Because different materials can be freely stacked, van der Waals materials are attracting attention as key building blocks for next-generation AI chips and ultra-low-power semiconductor devices. However, their chemically stable surfaces make it difficult to grow new semiconductor layers in a uniformly aligned orientation. This challenge becomes even greater at lower temperatures, where atoms tend to nucleate and grow in random directions, making it difficult to produce high-performance semiconductor films. The research team developed a new method that allows tellurium (Te)-containing precursors—molecular building blocks used to fabricate semiconductors—to move freely across the surface, find the most energetically stable positions, and form a thin film. < Figure 1. Diffusion-steered epitaxial atomic layer deposition of tellurium and structural character of tellurium grown on WSe₂. The figure illustrates how precursors first adsorb weakly onto the van der Waals surface, diffuse across it, and initiate tellurium crystal growth at energetically stable positions. Electron microscopy and optical and structural analyses confirmed that the tellurium grew along a consistent crystallographic orientation, forming a clean interface between the two materials while maintaining extremely low interfacial distortion. The results also demonstrate that, unlike conventional high-temperature growth processes, this technique enables epitaxial growth at a low temperature of 150°C. > Tellurium is attracting attention as a key material for next-generation semiconductors and optoelectronic devices, including photodetectors and light-emitting diodes (LEDs), because it combines highly direction-dependent electrical conductivity with excellent light-controlling properties. Using this approach, the research team succeeded in achieving epitaxial growth of tellurium uniformly in a single direction on van der Waals materials— next-generation two-dimensional semiconductor materials composed of multiple atomically thin layers stacked like sheets of paper— at a low temperature of 150°C using ALD. In epitaxial growth, a new semiconductor film grows in an ordered manner following the atomic arrangement of the underlying crystal, enabling the precise fabrication of high-quality semiconductor films. This process is comparable to stacking bricks neatly in the same direction rather than placing them randomly, which can improve electrical transport and enhance semiconductor performance. The researchers also confirmed that the technology could be applied to a range of van der Waals materials, including tungsten diselenide (WSe2), molybdenum disulfide (MoSS), rhenium diselenide (ReSe2), and mica. This demonstrates that the method is not limited to a single material but can be broadly used with various next-generation semiconductor materials. The team further used the resulting semiconductor films to fabricate transistors, key semiconductor devices that control the flow of electrical current, as well as optoelectronic devices that detect or emit light. This demonstrated that the new manufacturing technology is not confined to laboratory-scale material growth but can also be applied to the fabrication of functional semiconductor devices. < Figure 2. Conceptual illustration of tellurium thin-film growth using diffusion-steered epitaxial atomic layer deposition. The image depicts the process in which two types of precursors are supplied onto a van der Waals surface, diffuse across the surface, and form a tellurium thin film aligned in a consistent crystallographic direction at energetically stable positions. (AI-generated image) > "This study is the first to demonstrate that high-quality semiconductor films can be grown on van der Waals materials at low temperature without damaging the underlying materials," said Professor Suh. "We expect this technology to serve as a key manufacturing platform for integrating a wide range of next-generation semiconductors on a single chip," he added. The study, with Changhwan Kim, a doctoral student, as first author and Professor Suh as corresponding author, was published in the journal Science Advances on July 24. Paper title: Van der Waals template-encoded soft epitaxy of tellurium enabled by atomic layer deposition DOI: 10.1126/sciadv.aef1430 Author information: Changhwan Kim (Korea Advanced Institute of Science and Technology / Ulsan National Institute of Science and Technology, first author) and Joonki Suh (Korea Advanced Institute of Science and Technology, corresponding author) This research was supported by the National Research Foundation of Korea, under the Ministry of Science and ICT.

KAIST Develops Ultra-Precise Inspection Technology..

< The research team. From left: Jinwoo Jeon, a student in the integrated M.S.–Ph.D. program; Dr. Younggeun Lee; Dr. Guseon Kang; Professor Young-Jin Kim; Jaeyoon Kim, a Ph.D. student; and Dr. Dongwook Yang. > An ultra-precise inspection technology that could help prevent electric vehicle battery fires and improve battery safety has been developed. A KAIST research team has developed a method capable of detecting minute variations in battery electrode thickness that can contribute to thermal runaway with a precision equivalent to approximately one ten-thousandth the diameter of a human hair, all without disassembling or damaging the battery. The technology is expected to improve battery safety and quality by identifying invisible defects during the manufacturing process. KAIST (President Choongsik Bae) announced on 23rd of July that a research team led by Professor Young-Jin Kim from the Department of Mechanical Engineering has developed a technology that measures the thickness of lithium-ion battery electrodes in a non-contact and non-destructive manner. The technology combines terahertz waves (electromagnetic waves in the spectral region between light and radio waves) to obtain information from inside battery electrodes with an optical frequency comb, which divides the frequency of light into evenly spaced intervals like the markings on a ruler and serves as a reference for ultra-precise measurements. The electrodes in lithium-ion batteries, which are widely used in electric vehicles, are essential components through which electric current flows. Even a slight variation in electrode thickness can cause current to become concentrated in certain areas when charging and discharging, generating heat. If the heat continues to accumulate, it may lead to thermal runaway, a phenomenon in which the internal temperature of a battery rises rapidly and can result in a fire or explosion. Maintaining uniform electrode thickness is therefore critically important during battery manufacturing. Existing inspection technologies, however, have limitations when applied to production environments. X-ray computed tomography can provide detailed images of internal structures, but its relatively long inspection time makes it difficult to use on high-speed production lines. Ultrasonic acoustic microscopy requires direct contact with a liquid medium, while laser displacement sensors can perform rapid measurements but have difficulty precisely analyzing structures inside an electrode. The research team overcame these limitations by combining optical frequency comb and terahertz technologies. The researchers first directed terahertz waves at a battery electrode and collected signals generated as the waves were repeatedly reflected within the electrode. They then used an optical frequency comb as a reference to analyze the signals with exceptionally high precision and calculate the electrode thickness. This enabled nanometer-scale measurements of the electrode’s internal structure without damaging the battery. At the core of the technology is Fabry–Pérot interference, a regularly spaced interference pattern produced as terahertz waves repeatedly travel back and forth between the front and rear surfaces of an electrode. Much like measuring length by reading the markings on a ruler, the researchers precisely analyzed the interference pattern using the optical frequency comb as a reference to determine the electrode thickness. As a result, the team successfully measured both the electrode thickness and its complex refractive index (a material’s optical property indicating how strongly it transmits and absorbs electromagnetic waves) in a single measurement without requiring a separate calibration process. The researchers validated the technology using battery electrodes measuring between 50 and 150 micrometers in thickness, comparable to the diameter of a human hair. With a measurement time of just 0.2 seconds, the system detected thickness differences as small as 70.1 nanometers in the anode (approximately one fourteen-hundredth the diameter of a human hair) and 465.5 nanometers in the cathode. This measurement speed is considered sufficient for use on rapidly moving battery production lines. When the measurement time was increased to 25.6 seconds, the precision improved further. The system distinguished differences as small as 7.8 nanometers in the anode (approximately one ten-thousandth the diameter of a human hair) and 25.2 nanometers in the cathode. This represents up to a 100-fold improvement in precision compared with conventional time-domain analysis methods, enabling the detection of thickness variations that are completely invisible to the naked eye. The technology is not limited to measuring thickness at a single point. It can generate a three-dimensional map of thickness across an entire electrode and track gradual thickness variations in real time during production. The researchers also confirmed that the system could accurately measure an electrode tilted at an angle of approximately 45 degrees, demonstrating its potential for application to fast-moving, real-world battery manufacturing lines. < 1. Lithium-Ion Battery Electrode Thickness Measurement Using Optical-Frequency-Comb-Referenced Terahertz Fabry–Pérot Interferometry > The study is significant because it presents a new inspection technology capable of identifying invisible microscopic defects during production without disassembling or damaging batteries. In addition to lithium-ion batteries, the technology is expected to serve as a key quality-control tool for manufacturing next-generation all-solid-state batteries, which use solid electrolytes instead of liquid electrolytes. By detecting defects at an early stage, the technology could improve battery safety and quality while enabling more stable manufacturing processes. “This technology is an integrated metrology platform that can simultaneously measure electrode thickness and material properties without requiring a separate calibration process,” said Professor Kim. “We expect it to become a key technology for the real-time quality control of production lines for next-generation lithium-ion batteries and all-solid-state batteries.” The study was led by Dr. Guseon Kang from the KAIST Department of Mechanical Engineering, currently with the Korea Institute of Industrial Technology, as the first author, with Professor Young-Jin Kim serving as the corresponding author. The research findings were published in the international journal Nature Communications on June 10. Paper title: Nanometre-precision terahertz interferometry for battery electrode metrology DOI: https://doi.org/10.1038/s41467-026-74193-8 This work was financially supported by the National Research Foundation of Korea (NRF) (RS-2024-00401786, RS-2025-00523273, RS-2025-25455397, RS-2026-25540567, and NRF-2022M1A3C2069728) and from the Korean government’s Defense Acquisition Program Administration (DAPA) (KRIT-CT-22-040).