International Affairs Team

Abstract Quantum dots (QDs) are fluorescent nanoparticles widely used for single-molecule imaging because of their exceptional brightness and photostability. However, the impact of QD surface chemistry on biomolecular interactions has not been systematically investigated. Here, we report that commercial QDs unexpectedly destabilize protein-DNA complexes by inducing protein dissociation from DNA. Using the human nucleotide excision repair protein, xeroderma pigmentosum complementation group A (XPA) as a model system, we demonstrate that antibody-conjugated QDs promote dissociation of XPA from DNA substrates, independently of sizes and surface modification of QDs, antibody types, epitope tags, buffer conditions, or DNA structures. We find that polyethylene glycol (PEG), a common polymer coating on QD surfaces, is the primary factor responsible for this effect. To tackle this problem, we engineered QDs with precisely controlled surface polymer compositions. By systematically changing the ratio of anchoring, hydrophilic, and PEG-based functional groups, we find that reducing PEG density below a critical threshold effectively suppresses protein dissociation while maintaining excellent colloidal stability and brightness. Furthermore, antibodies conjugated via click chemistry between azide groups and DBCO enabled specific labeling of XPA without perturbing the DNA binding activity. Using these optimized QDs, we conducted single-molecule DNA curtain assays to visualize XPA-DNA interactions. QD-labeled XPA exhibits one-dimensional diffusion with frequent pausing on undamaged DNA. DNA curtain assays revealed that XPA preferentially binds DNA bubbles and searches for bubble structures through both one-dimensional diffusion and three-dimensional collision. Quantitative analysis showed that three-dimensional collision is the dominant pathway for bubble recognition. Taken together, our results uncover a previously unrecognized limitation of PEG-coated QDs in single-molecule studies and provide an improved surface-engineering strategy to preserve native protein-DNA interactions. Newly engineered QDs establish robust platforms for accurate single-molecule visualization of biomolecular processes. Quantum dots (QDs) are widely used as fluorescent probes for single-molecule imaging because they are brighter and more photostable than conventional fluorescent dyes. Researchers at UNIST have now discovered that the surface chemistry of many commercially available QDs can unintentionally disrupt the protein–DNA interactions they are intended to observe. The team also established a surface-engineering strategy that minimizes this interference without compromising their imaging performance. Led by Professor Jaeil Lee of the Department of Biological Sciences and Professor Jongnam Park of the Department of Energy and Chemical Engineering, the researchers identified polyethylene glycol (PEG), a polymer widely used to coat commercial QDs, as the source of the interference. When PEG occupied too great a proportion of the particle surface, protein–DNA interactions became destabilized, revealing a previously unrecognized limitation of a widely used imaging probe. The finding arose while the team was investigating xeroderma pigmentosum complementation group A (XPA), a DNA repair protein involved in nucleotide excision repair. As the concentration of commercially available QDs increased, XPA unexpectedly dissociated from DNA. The same behavior persisted regardless of quantum dot size, antibody type, surface modification, buffer conditions, or DNA structure, indicating that the disruption originated from the probe itself rather than the biological system. To address this limitation, the researchers systematically redesigned the quantum dot surface by adjusting the proportions of anchoring, hydrophilic, and PEG-derived functional groups. They found that reducing the PEG-derived surface composition below approximately 7% preserved native protein–DNA interactions while maintaining the brightness and colloidal stability required for single-molecule imaging. Using the redesigned QDs, the researchers visualized how XPA searches for sites of DNA damage without perturbing its natural behavior. Single-molecule DNA curtain experiments showed that XPA locates DNA bubble structures through both one-dimensional diffusion along DNA and three-dimensional collisions from solution. Quantitative analysis revealed that three-dimensional collisions are the dominant pathway for recognizing damaged DNA under physiologically relevant protein concentrations. “Commercial quantum dots have long been regarded as passive fluorescent probes,” said Professor Lee. "Our study shows that their surface chemistry can directly influence the molecular interactions being observed. By identifying the source of this interference, we established a practical strategy for preserving native protein–DNA interactions during single-molecule imaging." Professor Park added, "Careful control of surface polymer composition allowed us to reduce molecular interference without compromising brightness or colloidal stability. We expect these design principles to support the development of more reliable nanoparticle-based probes for biological imaging." The study also included contributions from Youngseo Kim of the Department of Biological Sciences and Hyerim Kim of the Department of Energy and Chemical Engineering at UNIST. The findings were published online in Nano Convergence on June 3, 2026. The research was supported by the National Research Foundation of Korea (NRF), the Institute for Basic Science (IBS), and the Ministry of Trade, Industry and Energy (MOTIE). Journal Reference Youngseo Kim, Hyerim Kim, Munryul Choi, et al ., "Engineering quantum dot surfaces to preserve protein-DNA interactions for single-molecule visualization," Nano Converg ., (2026).

Abstract Wrinkle-based diffraction gratings offer a scalable route to pigment-free structural coloration, yet most platforms are limited by globally aligned, linear wrinkles that provide limited spatial programmability and narrow angular selectivity. Here, we report geometrically programmed wrinkle architectonics in a UV-curable chitosan (UVCC)/polydimethylsiloxane (PDMS) bilayer, where lithography-defined, “geometric voids” serve as boundary constraints and stress concentrators to deterministically steer wrinkle trajectories during mechanical deformation. Simple geometric primitives effectively reshape the local stress field through geometrically incompatible confinement, transforming 1D gratings into shape-specific, spatially bifurcated domains characterized by curvilinear orientation and straight-wrinkle fields. We quantify these trajectory fields using the curvature metric and map critical design levers, such as inter-pattern spacing, UVCC thickness, and pattern asymmetry, that modulate curvature magnitude and steering range without altering diffraction periodicity. Circular perimeters sustain extended semi-circular trajectories, whereas polygonal vertices concentrate stress to induce aggressive local curvature. This programmed orientation dramatically amplifies angular dispersion: symmetric circular arrays achieve peak viewing-angle responsivity nearly three times higher than that of unpatterned thin films. A macaw security motif confirms wide-angle azimuthal readability and the formation of device-unique optical fingerprints for anti-counterfeiting. This work transforms a ubiquitous mechanical instability into a geometry-addressable photonic platform for high-level authentication and advanced displays. Hidden optical images that appear only when a transparent film is bent could provide a new generation of anti-counterfeiting technologies. Researchers at UNIST have demonstrated a strategy for programming the microscopic wrinkle patterns responsible for structural color, enabling optical films with enhanced viewing-angle sensitivity and unique optical fingerprints. Led by Professor Taesung Kim of the Department of Mechanical Engineering, the researchers discovered that simple geometric patterns can precisely control how microscopic wrinkles form as the film bends. By reshaping local mechanical stresses, the patterned boundaries guide wrinkle growth into both straight and curved domains while preserving the regular spacing responsible for structural color. This independent control over wrinkle orientation expands the film's optical capabilities. The enhanced wrinkle architecture translated directly into a broader optical response. The engineered films required only seven degrees of viewing-angle change to display the full visible color spectrum, compared with approximately thirty degrees for conventional wrinkle-based films. Distinct color changes also appeared with angular shifts as small as one degree, allowing substantially more optical information to be encoded within the same viewing range. To demonstrate these capabilities, the team fabricated a transparent security film featuring a hidden macaw image that appeared only when the film was bent. Unlike conventional wrinkle-based security films, which are visible only over a narrow range of viewing angles, the new design maintains clear image visibility and vivid structural colors across viewing angles from 0° to 90°. Each film also developed subtle variations in wrinkle branching, creating device-specific optical fingerprints that provide an additional layer of authentication beyond the visible security image. The films also showed excellent mechanical durability, maintaining stable wrinkle patterns and optical performance after 500 repeated bending cycles. “Instead of treating wrinkles as an unavoidable mechanical instability, we showed that they can be programmed through geometric design,” said Professor Kim. “By controlling wrinkle orientation while preserving wrinkle periodicity, we significantly broadened the optical response of wrinkle-based structural color films.” He added, "The approach could be applied to anti-counterfeiting technologies for banknotes, identification cards, luxury goods, and pharmaceutical packaging, while also providing a versatile platform for optical sensors and flexible display technologies." The findings were published in Advanced Functional Materials on June 25, 2026. Dr. Kaliannan Thiyagarajan and Sungjoon Ji from UNIST served as co-first authors of the study. The research was supported by the National Research Foundation of Korea (NRF) and UNIST. Journal Reference Kaliannan Thiyagarajan, Sungjoon Ji, Jiseok Han, et al ., “Geometrically Programmed Wrinkle Architectonics for High Angle-Dependent Structural Coloration,” Adv. Funct. Mater. , (2026).

Abstract Robust reinforcement learning (RRL) aims to tackle unexpected environmental changes by optimizing policies against the worst case. However, RRL remains impractical due to the cost of the Max-Min optimization, where it suffers from the exhaustive query complexity for finding the worst-case (dubbed 'Min') within the environmental uncertainty set U , ie, O (|U|). By viewing this via a lens of quantum perspective, we raise a pivotal question: If we can query from the environment with quantum superpositions, is it possible to accelerate the Max-Min optimization of RRL? Our answer is 'Yes'. Our method, called quantum robust inner minimization (QRIM), encodes the uncertainty set with quantum superposition and amplifies low-return cases, thus enabling RL for solving the robust (ie, worst-case) Bellman equation. Importantly, QRIM achieves a quadratic speed-up in query complexity without altering the outer RL pipeline, ie, O (|U|). Validated through classical simulations to real quantum hardware execution, QRIM learns more robust policies with quadratically reduced queries than classical RL. Artificial intelligence systems deployed in the real world must continue to make reliable decisions even when conditions differ from those encountered during training. Robust reinforcement learning (RRL) addresses this challenge by preparing AI agents for the worst-case scenarios they may face. However, identifying those worst-case conditions requires searching a large uncertainty space, making training increasingly expensive as the number of possible environmental variations grows. To this bottleneck, Professor Sung Whan Yoon of the Graduate School of Artificial Intelligence at UNIST, in collaboration with Professor Joongheon Kim of Korea University, developed Quantum Robust Inner Minimization (QRIM), a method that replaces only the inner optimization responsible for identifying worst-case environments, while leaving the overall reinforcement learning pipeline unchanged. QRIM exploits quantum superposition to evaluate many possible environmental conditions simultaneously rather than examining each one individually. By amplifying the worst-case scenarios through a quantum search routine, the method reduces the number of required environment queries from N to √N—a quadratic improvement in query complexity—without modifying the surrounding reinforcement learning procedure. Across a series of experiments, QRIM learned more robust policies while requiring only 20–30% of the computation used by conventional methods. The researchers validated the algorithm through both classical simulations and experiments on IBM's 127-qubit quantum processor, demonstrating that the quadratic speed-up remained effective even in the presence of hardware noise. “QRIM redesigns the most computationally demanding component of RRL—the search for worst-case environments—using a quantum algorithm,” said first author Hyun Kyu Lee. "Because only the inner optimization is replaced, existing reinforcement learning algorithms can be readily incorporate the quantum module, making the approach broadly applicable across robust reinforcement learning." “This study demonstrates how quantum computing can complement artificial intelligence by addressing a well-defined computational bottleneck,” said Professor Yoon. "As quantum hardware continues to advance, approaches like QRIM could help make RRL practical for applications that require reliable decision-making under uncertainty, including robotics and autonomous driving." The findings have been accepted for presentation at the International Conference on Machine Learning (ICML) 2026, one of the world's leading conferences on machine learning. Among the quantum AI papers accepted to ICML 2026, QRIM is the only study led by a Korean research institution. The research was supported by the National Research Foundation of Korea (NRF) and the Institute for Information & Communications Technology Planning & Evaluation (IITP) through programs funded by the Ministry of Science and ICT (MSIT). Journal Reference Hyun Kyu Lee, Joongheon Kim, and Sung Whan Yoon, “Quantum Robust Inner Minimization for Reinforcement Learning with Quadratic Speed-Up in Query Complexity,” ICML, (2026) .

Abstract The explosive growth of open-source model repositories has created a Model Jungle, where checkpoints are frequently shared without adequate documentation or metadata. While weight-space learning offers a pathway to identify and analyze these models directly from their parameters, processing full-scale weights is computationally prohibitive. Probing-based methods have emerged as a lightweight alternative, extracting permutation-equivariant representations via learnable probe vectors. However, existing probing methods are limited by a single-view design: they capture first-order structures but fail to encode the rich, higher-order correlation patterns inherent in row-column interactions. To bridge this gap, we introduce MVProbe, a multi-perspective probing framework that synthesizes first-order signals with interaction-aware (Gram-based) views. Our approach is theoretically grounded; We analyze the scaling laws of different probing orders to derive a principled standardization and fusion strategy that ensures balanced contributions from all branches. On the Model Jungle benchmark, MVProbe consistently outperforms the state-of-the-art ProbeX across diverse architectures, including discriminative backbones (ResNet, SupViT, MAE, DINO) and large-scale generative LoRA adapters (Stable Diffusion LoRA). Not every AI model comes with a manual. As open-source model repositories continue to grow, many checkpoints are shared with little information about how they were trained or what data they learned from. As a result, identifying suitable models for reuse—or tracing where they came from—is becoming increasingly difficult. To address this challenge, Professor Jaejun Yoo and his team at the UNIST Graduate School of Artificial Intelligence developed MVProbe (Multi-view Probe), a method that identifies what an AI model has learned by analyzing its internal parameters alone, without relying on metadata or executing the model itself. Conventional probing methods analyze model weights from only a single perspective, limiting their ability to distinguish between closely related models. MVProbe overcomes this limitation by examining neural network parameters from four complementary perspectives, capturing not only individual parameter values but also the relationships among them to build a more complete representation of a model. The researchers also showed why existing probing methods struggle to distinguish similar models, demonstrating mathematically that important information embedded in model parameters is overlooked when weights are analyzed from only a single perspective. By integrating multiple complementary views, MVProbe overcomes this limitation while remaining reliable regardless of which neural network layer is analyzed. The team evaluated MVProbe on the Model Jungle benchmark, a large collection of modern AI models spanning image recognition networks and large generative models. Across all evaluated architectures, the method consistently outperformed the previous state-of-the-art approach, improving identification accuracy by as much as 5.96 percentage points while maintaining stable performance across different network layers. Beyond making large model repositories easier to search and organize, MVProbe could support a wide range of applications, including model reuse, provenance analysis, copyright verification, and AI security auditing—all without requiring models to be executed. “As more pretrained AI models become publicly available, the ability to identify and reuse the right one is becoming increasingly important,” said Professor Yoo. "MVProbe makes it possible to search and manage large model repositories by reading the information already encoded in a model's parameters, without running the model itself." The study was co-first authored by Eunwoo Heo and Kyungguk Seo of the UNIST Graduate School of Artificial Intelligence. It has been accepted for presentation at the International Conference on Machine Learning (ICML) 2026. This work was supported by the Institute for Information & Communications Technology Planning & Evaluation (IITP) and the National Research Foundation of Korea (NRF). Journal Reference Eunwoo Heo, Kyeongkook Seo, and Jaejun Yoo, "What Linear Probes Miss: Multi-View Probing for Weight-Space Learning,"

Abstract Direct photopatterning with photoactivated cross-linkers provides a powerful route to highly integrated, solution-processed electronics. We introduce Diazo-6Bx, a six-branched diazo-based cross-linker that combines high miscibility with organic semiconductors and efficient C–H insertion reactivity. Comparative studies with azide-functionalized cross-linkers (phenyl- and tetrafluorophenyl-based structural analogues, namely, Ph-6Bx and 6Bx, respectively) reveal that the fluorine-free structure of Diazo-6Bx and its superior compatibility with host materials enable highly uniform cross-linking. This results in high-fidelity photopatterning with steep taper angles, excellent pattern retention, and minimal line-width variation. In solution-processed organic thin-film transistors (OTFTs), Diazo-6Bx enables the fabrication of unit devices that outperform those based on 6Bx, as well as large-area, high-resolution transistor arrays and logic circuits with excellent operational stability and scalability. These results demonstrate that a careful cross-linker design will play a crucial role in patterning next-generation electronic materials in extremely high fidelity. Printing electronic circuits from solution offers a promising route to lightweight, flexible electronics, but producing fine circuit patterns without sacrificing device performance remains a persistent manufacturing challenge. Professor BongSoo Kim and his research team in the Department of Chemistry at UNIST, working with collaborators at Yonsei University, have developed a new photo-crosslinker that enables more precise patterning of organic semiconductor circuits while improving the stability of the resulting devices. The material, Diazo-6Bx, was designed to blend more uniformly with hydrocarbon-based organic semiconductors than existing photo-crosslinkers. During ultraviolet patterning, it forms cross-linked networks that preserve circuit geometry, producing cleaner edges and more consistent feature sizes. In photopatterning experiments, Diazo-6Bx reduced line-width variation from 10.3 μm to 2.5 μm—roughly one quarter that of a widely used conventional photo-crosslinker. Circuit edges also became significantly sharper, with taper angles increasing from 67.9° to 87°, approaching nearly vertical sidewalls. The improved material compatibility also reduced charge-trapping defects, resulting in more stable transistor performance during prolonged operation. Organic thin-film transistors fabricated using Diazo-6Bx maintained more stable electrical characteristics under prolonged operation than devices produced with conventional crosslinking materials. The researchers also demonstrated that both p-type and n-type organic semiconductors could be patterned sequentially using the same solvent process. Using the same process, the researchers fabricated an 84-transistor organic thin-film transistor (OTFT) array together with complementary NOT, NAND, and NOR logic gates, demonstrating the scalability of the approach for complex organic electronic circuits. “This photo-crosslinker improves both patterning precision and device stability,” said Professor BongSoo Kim of the Department of Chemistry at UNIST. "Its compatibility with both p-type and n-type organic semiconductors simplifies solution processing and could help simplify manufacturing of large-area flexible displays and wearable electronic devices." The study was conducted in collaboration with Professor Jeong Ho Cho of Yonsei University. The findings were published in ACS Nano on June 23, 2026. The research was supported by the Samsung Future Technology Incubation Program and the National Research Foundation of Korea (NRF) through funding from the Ministry of Science and ICT (MSIT). Journal Reference Hyobin Ham, In Cheol Kwak, Daeyeon Lee, et al. , “Diazo-6Bx, a Six-Branched Diazo Cross-Linker, Enables High-Fidelity Patterning for Solution-Processed Electronics with Stable Operation,” ACS Nano, (2026).

Abstract A proposed chest patch device and its interface IC are configured to allow detection of multi-domain signals in the form of optical, electrical, acoustic, and chemical signals at a single body spot. The IC integrates on-chip classification capabilities for cardiovascular diseases (CVDs) based on photo-plethysmogram (PPG) and electrocardiogram (ECG), along with hazardous gas analysis. For low-power multimodal sensing, an ECG R-peak_triggered PPG window (RPT-PW) is proposed as an inter-sensor scheme to reduce activity on the PPG channel, which is the dominant energy consumer. Additionally, the PPG and ECG readout channels incorporate analog peak detection to enable normalized on-chip extraction of pulse arrival time (PAT) and RR interval (RRI). For minimal computation and communication power, a multi-domain convolutional neural network (MD-CNN) processes both analog and digital inputs, and a reconfigurable analog ternary/binarized neural network (a-TNN/BNN) provides additional computation capability for multi-domain applications. The IC was fabricated in an 180-nm BCD process and integrated into a chest patch device prototype with an in-house adhesive meta patch. The RPT-PW demonstrated adaptive operation with an effective LED duty cycle (EDC) as low as 0.02%. The on-chip computation achieved average sensitivity and specificity of 90.87%/95.38% for three-label hypertension, 92.80%/96.36% for three-label arrhythmia, and 92.46%/97.50% for four-label gas mixture classification. Continuous wearable monitoring often requires transmitting large volumes of sensor data to external devices for analysis, increasing power consumption and limiting operating time. A joint research team, led by Professor Jae Joon Kim of Electrical Engineering and Professor Hoon Eui Jeong of Mechanical Engineering, has developed a wearable chest patch that analyzes physiological and environmental signals directly on the device, reducing both communication and sensing power while enabling continuous real-time monitoring. The wearable integrates optical, electrical, acoustic, and chemical sensing into a single device capable of simultaneously monitoring cardiovascular signals and hazardous gases. Rather than transmitting raw sensor data, it processes the information using an on-chip AI processor and sends only the analysis results via Bluetooth. This reduces communication overhead while allowing multiple users to be monitored remotely. To extend battery life, the researchers introduced an adaptive sensing scheme that selectively activates the optical sensor—the most power-intensive component of the device. By synchronizing optical measurements with ECG signals, the system reduced power consumption in the optical sensing channel by approximately 83%, enabling longer operation on a single charge. The device achieved more than 90% sensitivity and specificity in classifying hypertension and cardiac arrhythmias. It also classified hazardous gas mixtures with 92.46% accuracy, demonstrating its ability to monitor both physiological conditions and environmental hazards using a single wearable platform. The chest patch incorporates a microstructured adhesive interface that conforms securely to rough skin while allowing clean removal without leaving residue, making it better suited for long-term wear. “This work brings multimodal sensing and AI inference together on a single wearable platform,” said Professor Kim. “By processing information directly on the device, the system reduces communication demands while enabling continuous monitoring of both health conditions and environmental hazards.” The study has been participated by Sanghyeon Cho and Hyunjoong Kim as co-first authors. Their findings will appear in the July 2026 issue of the IEEE Journal of Solid-State Circuits (JSSC) , one of the leading journals in integrated circuit design. The technology has been licensed to Anvix Lab, a startup co-founded by the research team, which is pursuing commercialization of next-generation bioelectronic patch platform based on on-chip AI. The research was supported by the Ministry of Trade, Industry and Energy (MOTIE), the Ministry of Education (ME), and the Ministry of Science and ICT (MSIT). Journal Reference Sanghyeon Cho, Hyunjoong Kim, Dong Kwan Kang, et. al., “An Energy-Efficient Chest Patch Interface With Inter-Sensor PPG Windowing and Multi-Domain On-Chip Analog Computing,” JSSC , (2026).

Abstract A surge of recent advancements has consistently highlighted the superiority of multimodal learning over unimodal approaches across a variety of tasks. However, the theoretical foundations elucidating this advantage remain underexplored: existing theoretical analyzes are often constrained by tight assumptions, and lack empirical validation. In this paper, we bridge this gap by proposing a novel theoretical framework grounded in convolutional smoothing, offering a new perspective on how multimodal learning contributes to a smoother loss landscape compared to unimodal learning. Building upon this theoretical foundation, we introduce a simple yet effective distributional training strategy based on stochastic modality pairing instead of a fixed pairing; Thus, further promoting a flatter landscape via convolutional smoothing. Our empirical results across various multimodal datasets demonstrate that multimodal models not only achieve higher performance but also exhibit flatter loss landscape, which represent better generalization and robustness. Artificial intelligence models trained on combinations of images, speech, and text consistently outperform those that learn from a single type of data. While this advantage has been widely observed, the reason multimodal learning produces more accurate and reliable models has remained largely unexplained. Researchers at UNIST have now provided a mathematical explanation for this phenomenon and used it to develop a new training strategy that further improves multimodal learning. The study, led by Professor Sung Whan Yoon of the Graduate School of Artificial Intelligence, shows that learning from multiple data modalities produces a flatter loss landscape—a property associated with models that generalize more effectively to unfamiliar data and remain more resilient to noise and perturbations. The researchers demonstrate that this effect arises through a mathematical mechanism known as convolutional smoothing. By learning from different types of information simultaneously, multimodal models naturally smooth abrupt changes in the optimization process, creating a more stable learning landscape than models trained on a single modality. Building on this theoretical framework, the team developed Distributional Multimodal Learning (DML), a simple training strategy that replaces fixed image-text or image-audio pairs with randomly sampled combinations drawn from the same class. This broader variety of training examples further enhances the smoothing effect, leading to stronger generalization and more robust learning. Across multiple multimodal benchmark datasets, DML consistently outperformed conventional multimodal training. The method improved both classification accuracy and cross-modal retrieval tasks, including matching images with their corresponding text descriptions and retrieving images from textual queries. “Our work provides a theoretical foundation for understanding why multimodal learning consistently outperforms unimodal learning,” the research team said. “It also demonstrates how that understanding can be translated into a simple yet effective training strategy that improves both robustness and generalization.” The study's first author is Jaejun Lee, a researcher in the UNIST Graduate School of Artificial Intelligence. The work has been accepted for presentation at the International Conference on Machine Learning (ICML) 2026, one of the world's leading conferences on artificial intelligence, to be held in Seoul from July 6–11. The research was supported by the National Research Foundation of Korea (NRF) and the Institute for Information & Communications Technology Planning &Evaluation (IITP) through programs funded by the Ministry of Science and ICT. Journal Reference Jae-Jun Lee and Sung Whan Yoon, "Understanding Multimodal Learning: A Loss Landscape Smoothness Perspective," ICML '26 ., (2026).

Abstract The 2025 Los Angeles wildfires highlighted growing risks from climate-driven extremes and the need for reliable wildfire forecasting beyond short lead times. Although the European Center for Medium-Range Weather Forecasts provides global fire weather index forecasts, these products are primarily designed for long-range climate outlooks, and their practical effectiveness remains uncertain in regions with limited local forecasting infrastructure. Here we show that a global deep learning framework, forecasting daily fire weather index values up to 31 days ahead, consistently improves forecast accuracy and reduces bias relative to operational numerical forecasts. The framework learns nonlinear and lagged fire–weather relationships by integrating past fire weather index dynamics and future meteorological conditions. Importantly, forecast bias is reduced in 85% of grid cells where high wildfire exposure coincides with high socioeconomic vulnerability. This demonstrates that data-driven forecasting can help bridge critical information gaps in underserved regions and support more equitable climate risk management. Reliable wildfire forecasts become increasingly difficult beyond the first few weeks, limiting their value for medium-range decision-making. A research team, led by Professor Jungho of the Department of Civil, Urban, Earth, and Environmental Engineering at UNIST, has shown that a global deep learning framework can extend reliable daily wildfire forecasts to 31 days while reducing forecast bias compared with existing operational forecasting systems. Known as FWI-Net, the framework predicts daily values of the Fire Weather Index (FWI), a widely used measure of wildfire danger. Unlike conventional forecasting methods, it combines historical fire-weather conditions with future meteorological forecasts, allowing it to capture the cumulative effects of prolonged heat and drought on future wildfire risk. Across the full 31-day forecasting period, FWI-Net reduced prediction error by 6.6% compared with the operational forecasting system of the European Center for Medium-Range Weather Forecasts (ECMWF). During the first week, prediction error decreased by 12.4%. The model also extended the period of meaningful forecasts under very high wildfire danger by five days. The model performed particularly well in regions where high wildfire exposure coincides with high socioeconomic vulnerability. Forecast bias was reduced across 85% of these areas, and in regions with limited forecasting infrastructure, FWI-Net maintained useful forecasting skill for an average of 22 days. “By combining historical fire-weather conditions with future weather forecasts, the model captures patterns that conventional forecasting systems often miss,” the research team said. “Most importantly, it provides more reliable forecasts for regions facing high wildfire risk despite limited forecasting infrastructure.” “As climate change increases wildfire risk around the world, reliable forecasting is becoming essential for disaster preparedness,” said Professor Im. “We expect this framework to support medium-range wildfire planning while helping reduce information gaps in regions with limited forecasting capacity.” The study was co-led by Professor Yoojin Kang of Kookmin University and Sihyun Lee of UNIST, who served as first authors. The findings were published online in Communications Earth & Environment on June 28, 2026. The research was supported by the Ministry of Environment (ME), the Korea Forest Service, and the National Research Foundation of Korea (NRF). Journal Reference Yoojin Kang, Sihyun Lee, Dongjin Cho, and Jungho Im, "Deep learning-based forecasting provides a pathway to closing wildfire information gaps in underserved regions," Commun. Earth Environment. , (2026).

Abstract We demonstrate that in knowledge distillation for diffusion models, the teacher network's highly complex denoising process—stemming from its substantially larger capacity—poses a significant challenge for the student model to faithfully mimic. To address this problem, we propose a coarse-to-fine distillation framework with LInear Fitting-based distillation (LIFT) and Piecewise Local Adaptive Coefficient Estimation (PLACE). First, LIFT decomposes the objective into a coarse'' alignment and afine'' refinement. The student is then trained on coarse alignment before proceeding to hard refinement. Second, LInear Fitting-based distillation extends LIFT to address spatially non-uniform errors by partitioning outputs into error-based groups, providing locally adaptive guidance. Our comprehensive experimental results demonstrate that ours, \core~with \pick, outperforms previous knowledge distillation on diffusion models based on both U-Net and DiT architectures. Furthermore, as compression rates become exceedingly high, conventional knowledge distillation fails to provide sufficient guidance, thereby preventing lightweight diffusion models from achieving stable training. In contrast, our method demonstrates stable convergence even under such extreme compression ratios. Compact generative AI models promise to bring image generation to personal devices, but reducing model size often comes at the expense of image quality and training stability. Researchers at UNIST have developed a new knowledge distillation framework that enables lightweight diffusion models to learn more effectively from much larger models without increasing inference costs. Led by Professor Jaejun Yoo of the Graduate School of Artificial Intelligence, the study addresses a key challenge in knowledge distillation—the process of transferring the capabilities of a large AI model to a smaller one. The researchers found that as teacher models become increasingly capable, the complexity of their denoising process makes it progressively more difficult for compact student models to reproduce faithfully. Rather than asking the student model to reproduce every detail from the outset, the framework restructures learning into successive stages, allowing it to first capture an image's overall structure before progressively refining finer details. The approach further improves learning by providing additional guidance where prediction errors are greatest while adapting supervision throughout training. This strategy is implemented through two complementary techniques, LIFT (LInear Fitting-based Distillation) and PLACE (Piecewise Local Adaptive Coefficient Estimation). The approach changes only the training strategy, requiring neither additional model parameters nor extra computation during image generation. Across multiple image-generation benchmarks, the proposed framework consistently outperformed existing knowledge distillation methods. It proved effective for text-to-image synthesis, class-conditional generation, and unconditional image generation, while remaining compatible with multiple diffusion architectures, including U-Net, DiT, and MMDiT, the architecture used in Stable Diffusion 3. The advantages became particularly evident under extreme model compression, where the widening gap between teacher and student models often leaves conventional knowledge distillation unable to provide sufficient guidance for stable learning. When compressing a teacher model containing 78.7 million parameters into a student model with just 1.3 million parameters, the proposed method maintained stable convergence and generated substantially higher-quality images. “Rather than modifying model architectures, our approach improves how compact models learn from larger ones,” said Professor Yoo. “Because it introduces no additional model parameters or inference-time computation, it can be readily applied across a wide range of generative AI models, making compact generative AI practical across a wide range of computing environments.” Hyunsoo Han and Sangyeob Yeo of the Graduate School of Artificial Intelligence at UNIST contributed to the study. The findings have been accepted for presentation at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026. Journal Reference Hyunsoo Han, Sangyeop Yeo, and Jaejun Yoo, "LIFT and PLACE: A Simple, Stable, and Effective Knowledge Distillation Framework for Lightweight Diffusion Models," CVPR'26

Abstract Cooperation is central to multi-agent reinforcement learning (MARL), yet learned coordination can be fragile when external perturbations disrupt inter-agent interactions. Prior robust MARL methods have primarily considered value-oriented attacks, leaving a gap in robustness when interaction structures themselves are corrupted. In this paper, we propose an interaction-breaking adversarial learning (IBAL) framework that takes an information-theoretic view to construct attacks that impede coordination by perturbing agents' observations and actions, and trains agents to perform reliably under such disruptions. Empirically, our approach improves robustness over existing robust MARL baselines across diverse attack settings and yields stronger performance even under agent-missing scenarios. From autonomous drone swarms to industrial robots and self-driving vehicles, many AI systems depend on multiple agents working together. Yet when communication fails or individual agents become unavailable, coordination can quickly break down. Researchers at UNIST have developed a new reinforcement learning framework that trains AI agents to adapt to these disruptions, enabling them to continue working together under conditions that would typically cause conventional systems to fail. Led by Professor Seungyul Han of the Graduate School of Artificial Intelligence, the research team developed Interaction-Breaking Adversarial Learning (IBAL) , a multi-agent reinforcement learning framework that deliberately disrupts coordination during training. Rather than exposing agents only to ideal conditions, IBAL repeatedly simulates communication failures and missing teammates, encouraging the remaining agents to reorganize their roles and develop new cooperative strategies. Unlike previous robust reinforcement learning methods, which primarily strengthen individual agents against noisy observations or unfavorable actions, IBAL focuses on the interactions between agents. During training, the framework identifies information that is essential for coordination and selectively disrupts it, preparing AI systems to maintain cooperation even when communication becomes unreliable. "Existing robust multi-agent reinforcement learning methods mainly interfere with individual agents," said Sunwoo Lee, the study's first author. “IBAL instead challenges the interactions that enable cooperation, allowing the remaining agents to discover new ways of working together when communication is interrupted or some agents fail.” The researchers evaluated the proposed IBAL on the StarCraft II Multi-Agent Challenge (SMAC). In scenarios where some allied units suddenly became unavailable, conventional models often experienced cascading failures, with some achieving win rates as low as 13.3%. By contrast, agents trained with IBAL adapted their strategies in real time—reorganizing team formations and redistributing roles—and achieved a win rate of 87.0%. “Many real-world AI systems rely on multiple agents working together, making them vulnerable to equipment failures or communication loss,” said Professor Han. “Our framework trains AI agents to adapt to these unexpected disruptions and continue accomplishing their task, providing a foundation for safer and more reliable multi-agent systems.” The study has been accepted for presentation at the International Conference on Machine Learning (ICML) 2026, which will be held in Seoul from July 6 to 11, 2026. The research was supported by the Ministry of Science and ICT (MSIT), the Institute for Information & Communications Technology Planning & Evaluation (IITP), and the National Research Foundation of Korea (NRF). Journal Reference Sunwoo Lee, Mingu Kang, Yonghyeon Jo, and Seungyul Han, "Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning," ICML '26 , (2026).