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KAIST Develops AI to Detect ‘Foreign-Linked Opinion Manipulation’ in 110 Million News Comments
During election seasons or major national issues, online news comment sections often become heated spaces of conflict across gender, generation, and political lines. For years, there have been persistent concerns that behind some of these conflicts may lie “foreign winds,” or interventions by foreign actors seeking to manipulate public opinion and deepen social divisions. A KAIST research team has now developed a technology that uses big data on two decades worth of news comments and artificial intelligence (AI) to precisely detect traces of such hidden influence operations.
KAIST (President Choongsik Bae) announced on the 12th of August that a joint research team led by Professor Wonjae Lee of the Graduate School of Culture Technology, Professor Meeyoung Cha of the School of Computing (Scientific director at Max Planck Institute for Security and Privacy) and Professor Alice Oh of the School of Computing, in collaboration with Professor Thorsten Holz of the Max Planck Institute, has developed an explainable AI technology that automatically detects patterns suspected of foreign-linked influence operations in online news comments and provides specific evidence for its judgments.
The organized and repeated posting of comments or content by certain actors to shape public opinion in a desired direction is known as an “online influence operation.” Existing AI-based detection technologies have had a key limitation: even when they classify certain accounts as belonging to a “blacklist,” they often fail to provide clear evidence explaining why those accounts should be considered influence-operation accounts.
To overcome this limitation, the research team used 70 foreign-linked accounts previously identified by the Institute for National Security Strategy as starting points. They then tracked groups of accounts connected to them or repeatedly commenting on the same news articles, ultimately collecting and analyzing a large-scale dataset of 110 million comments posted on Naver News over a 20-year period from 2006 to 2025.
In particular, the AI developed by the research team examines accounts through a careful three-step process. First, it checks whether there are clues suggesting that the author may be linked to a foreign source. Second, it examines whether the comment contains emotionally polarizing expressions, such as moral condemnation or blind praise. Third, it identifies which country or target the emotional framing is directed toward.
The model does not stop at simply labeling an account as suspicious. It also highlights the specific phrases in the comments that served as the basis for its judgment. The team further combined this with multidimensional behavioral-pattern analysis, including account activity frequency, account lifespan, and activity links with other suspected accounts. As a result, among approximately 4 million Naver News users, the model ultimately identified 23,998 accounts exhibiting patterns consistent with suspected public-opinion manipulation.
The analysis also revealed the more subtle strategy of these suspected accounts. Their main target was not the victory of a particular political camp, but rather the maximization of division and confrontation within Korean society.
Among the top 10 targets that drew the highest public engagement, measured through likes and other reactions, seven were prominent domestic political figures. Notably, the attacks were not concentrated on a single party or ideology. Former and current presidents, presidential candidates, and political parties from both progressive and conservative camps were targeted across the spectrum. According to the research team, this suggests a sophisticated strategy aimed not so much at supporting a particular group, but at inflaming domestic political conflict and increasing social distrust and polarization.
This study is significant because it provides data-based evidence for influence-operation activity that had previously been discussed largely in terms of suspicion, while also offering a potential defense mechanism for protecting healthy online public discourse. In the future, portal platforms and related organizations could use this technology during elections or national crises to monitor the influx of suspicious accounts in real time and prioritize the review of coordinated attacks against domestic political figures. However, the research team emphasized that the AI should not be used to block accounts indiscriminately, but rather as an explainable content-moderation tool that supports the judgment of expert reviewers.
Professor Wonjae Lee said, “By analyzing 20 years of data, we found that suspected accounts tended to use messages criticizing Korea and domestic political figures rather than directly praising foreign countries, and that these messages gained higher visibility,” adding, “This research can provide empirical criteria for when and which messages platforms and monitoring organizations should prioritize for review, especially during socially sensitive periods such as elections.”
Professor Alice Oh said, “This is a meaningful achievement in which AI precisely identified not only the surface meaning of words in massive comment datasets, but also subtle emotional patterns and organized behavioral signals intended to provoke conflict,” adding, “It can become a powerful defense system against online influence operations, which are becoming increasingly sophisticated.”
Professor Meeyoung Cha said, “This study goes beyond simple blacklist-account analysis and represents the outcome of actionable data science that addresses real-world problems and drives practical change,” adding, “In an online environment where social conflict is deepening, we hope this technology will serve as a practical tool for protecting the transparency and trustworthiness of the digital public sphere.”
This research was led by KAIST Ph.D. candidate Jaehong Kim and master’s student Hyeonseung Kim as co-first authors. The paper is scheduled to be presented at the USENIX Security Symposium 2026, one of the most prestigious conferences in the field of computer security.
Paper title: Cross-National Information Attacks: A Two-Decade Analysis of Troll Behavior in Korea,
DOI: 10.48550/arXiv.2606.22785
This research was supported by the Hyundai Motor Chung Mong-Koo Foundation, the Institute of Information & Communications Technology Planning & Evaluation, and the National Research Foundation of Korea, funded by the Ministry of Science and ICT.
2026.08.12 View 153 -
KAIST Shapes a " Templates a ‘Gas Lattice’ in Porous Materials”: The Moment Gas Forms a Crystal-like Lattice
Capturing carbon or storing hydrogen to combat global warming requires compressing gases into sponge-like porous materials. Until now, gas molecules were thought to adsorb in a disordered manner throughout the pores. But what if invisible gas molecules could be lined up in regular order — like ice crystals or LEGO bricks?
KAIST (President Choongsik Bae) announced on August 11 that a research team led by Professor Jihan Kim of the Department of Chemical and Biomolecular Engineering has developed a computational framework that combines large-scale screening of metal–organic frameworks (MOFs)* with machine-learning-guided inverse design. Focusing on the “gas lattice”—a crystal-like ordered state formed by gas molecules under confinement—the framework enables researchers to explore a vast range of MOF structures and design candidate porous materials capable of stabilizing desired gas arrangements.
*Metal–organic framework (MOF): a material built from metal ions or clusters connected by organic linkers to create countless microscopic pores; MOFs are promising eco-friendly materials used to store or separate gases.
Using xenon (Xe), a monatomic noble gas, as a model system, the research team identified a specific cobalt-based porous material — Co-CAU-36 — that stabilizes xenon in a regular lattice. Computer simulations (GCMC) confirmed that xenon inside this material does not spread out randomly, but instead lines up in a body-centered cubic (BCC) lattice, a well-defined, crystal-like arrangement. This is a breakthrough because gas crystallization was achieved within the pores without the extreme bulk pressures normally required by using the pore structure as a ‘template’.
Striking results also emerged when the team examined the separation of xenon (Xe) and krypton (Kr), a gas mixture of industrial importance. Inside the framework, xenon preferentially occupies an ordered shell region, displacing krypton toward the pore core — a separation behavior that had not been reported before.
To show that the phenomenon could be deliberately designed rather than occurring incidentally, the researchers combined machine learning with a genetic algorithm and used inverse design to identify candidate porous structures targeting BCC- and FCC-like lattices.
The findings may have applications in advanced energy and environmental technologies that depend on precise control of molecular arrangement, including carbon capture and separation, selective catalytic reactions, and gas storage.
"This research is the first demonstration of a gas forming a crystal-like ordered state inside a porous material," said Professor Jihan Kim. He added that the work's significance lies in moving beyond conventional approaches focused primarily on increasing adsorption capacity, toward treating the arrangement of gas molecules itself as a design target.
"If this approach can be extended to more complex molecules, such as carbon dioxide or water, it could become an important starting point for designing tailored materials for gas separation and storage," Professor Kim added.
Younghun Kim and Dohoon Kim, PhD candidates in KAIST's Department of Chemical and Biomolecular Engineering, are co-first authors, with Seungwoo Kim, a master's candidate, and Yunsung Lim, a PhD, serving as co-authors. The findings were published online on June 23 in the international academic journal Nature Communications.
Paper title: Framework-templated gas lattices in metal-organic frameworks
DOI: 10.1038/s41467-026-74776-5This work was supported by grants from the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (Project Numbers RS-2024-00451160 and RS-2024-00435493).
2026.08.11 View 120 -
KAIST Held Inauguration Ceremony for 18th President Choongsik Bae, Unveiling Vision of "Fundamentals First, Innovation Forward"
KAIST announced that it held an inauguration ceremony for its 18th president, Choongsik Bae, at the KAIST Auditorium on Monday, August 10. At the ceremony, the university unveiled "Fundamentals First, Innovation Forward" as its new vision.
The ceremony officially presented President Bae's philosophy on university governance and his vision for KAIST's future to the KAIST community and the public. Departing from the conventional format of a formal inaugural address, President Bae personally explained his vision and the strategies for implementing it. Professor Yiyun Kang of the Department of Industrial Design directed the stage production, bringing KAIST's future vision to life through an intuitive and immersive presentation.
The event built on the innovation advanced under KAIST's 17th president, Kwang Hyung Lee, while introducing new leadership and development strategies that will guide the university toward its 60th anniversary. Distinguished guests from Korea and abroad attended, including Deputy Prime Minister and Minister of Science and ICT Kyung Hoon Bae, former KAIST President Kwang Hyung Lee, and ambassadors to Korea from key countries.
In his inaugural address, President Bae presented "Continuity & Innovation" as the central philosophy of his administration. He aimed to preserve the values KAIST has cultivated over the past 55 years -- Creativity, Challenge, and Caring -- while pursuing innovation across education, research, entrepreneurship, and administration in response to AI-driven transformation and intensifying global competition for technological leadership.
The new vision, "Fundamentals First, Innovation Forward," rests on two foundational principles: people strongly grounded in fundamental disciplines, humanistic insight, and AI capabilities; and an organization characterized by autonomy, accountability, and efficiency. On these foundations, KAIST aims to achieve world-class excellence in education, research, entrepreneurship, and internationalization.
To realize this vision, KAIST will pursue the following five development strategies, collectively called the Beyond Series:
Beyond AI – AI for Everyone: Create a leading environment for education and research that moves beyond today's AI toward Humanistic AI, Democratic AI, and Agentic AI.
Beyond Laboratory – Innovative Research and Entrepreneurship: Move beyond the laboratory to advance deep-tech innovation in partnership with industry and society and build a global startup ecosystem.
Beyond Barriers – An Efficient and Open University: Remove barriers so that members can devote themselves to research and education, underpinned by transparent governance and a culture and systems built on trust.
Beyond Carbon – Sustainability and a Greener Future: Strengthen research to address the climate crisis and create an environmentally responsible, carbon-neutral campus grounded in ESG and the UN Sustainable Development Goals.
Beyond KAIST – Toward the World and the Future through Global Connect: Connect global talent, universities, research institutions, companies, and local communities; foster a more international campus; expand international joint research; and strengthen global and regional partnerships.
KAIST plans to make AI not merely a technology for specific disciplines or specialists, but a common language and general-purpose tool across all fields. By strengthening foundational education and interdisciplinary AI education, KAIST aims to push beyond merely using AI effectively toward leading AI innovation.
KAIST will also expand research in physical AI, AI that operates in the real world, including robotics, autonomous driving, and advanced manufacturing, as well as in strategic technologies such as quantum science, climate technology, and energy technology. Building on world-class basic research, KAIST will expand industry collaboration, technology commercialization, and global entrepreneurship, creating a cycle in which research outcomes drive innovation in industry and society.
KAIST will expand the establishment of corporate satellite laboratories and collaborative research centers. It will also support joint research and development with companies by building AI Autonomous Labs that integrate AI into the R&D process, creating a new research environment in which AI designs and conducts experiments and analyzes the results. The university will introduce specialized entrepreneurship education for newly admitted students and establish a model that combines classroom instruction with hands-on training, involving alumni entrepreneurs and industry professionals.
The inauguration also featured case studies of KAIST alumni using AI to drive innovation in industry and research. Dr. Hyeon-Sook Yoon from Korea Shipbuilding & Offshore Engineering (KSOE) presented the use of digital twins in the shipbuilding and maritime industries, while Dr. Ji-Yong Shin from Samsung Electronics' Semiconductor R&D Center discussed the use of AI in semiconductor manufacturing. Professor Joonsik Hwang of KAIST then discussed the development and applications of physical AI in automobiles, mobility, robotics, and other fields.
KAIST plans to build an AI Native Campus that organically connects AI Interactive Education in education, AI Autonomous Labs in research, AI Agent Administration in administration, and AI Energy Convergence in infrastructure.
KAIST will also build an AI-based digital administration system to streamline or eliminate unnecessary regulations and procedures so that faculty and students can focus more fully on education and research. The campus will also become a living lab where climate and energy technologies are developed and validated, while global cooperation will be strengthened by recruiting outstanding international students and faculty, expanding international joint research, and broadening dual-degree programs.
In his address, President Bae said, “We will carry forward the proud tradition we have inherited: our vision of becoming a Global Value-Creative Leading University and our C-Cube core values of Creativity, Challenge, and Caring. Building on this foundation, we will pursue the innovation needed to move toward our new goal, Fundamentals First, Innovation Forward.” He added, “Grounded in strong fundamentals across both our people and our institution, we will advance five strategic priorities—AI, global entrepreneurship, a stronger focus on education and research, sustainable growth, and internationalization—and further establish KAIST as a world-leading university.”
He also emphasized, “I will listen with an open mind and act with determination. As both a facilitator and a servant leader, I will empower every member of the KAIST community to pursue their aspirations with confidence and fulfillment. Together, we will take KAIST beyond innovation—establishing it as a university that sets new standards and a national innovation platform shaping the future of science and technology in Korea.”
President Bae is an internationally recognized mechanical engineer and energy scientist specializing in carbon-neutral transportation power systems and sustainable mobility technologies. He earned his bachelor’s and master’s degrees in aerospace engineering from Seoul National University and a Ph.D. in mechanical engineering from Imperial College London. Since joining KAIST in 1998, he has served in leadership roles including Chair of the Department of Mechanical Engineering, Dean of the College of Engineering, and Director of the Mobile Clinic Module Project during the COVID-19 pandemic, gaining broad experience in education, research, and university administration.
He has also contributed to energy and carbon-neutrality research and to national science and technology policy as chair of the International Energy Agency's Technology Collaboration Programme on Sustainable Combustion, chair of the Climate Division of the Ministry of Foreign Affairs' Science and Technology Diplomacy Advisory Committee, and chair of the Society of Carbon-Neutral Fuel Technology. He was the first Korean researcher in the powertrain field to be elected an SAE Fellow and has received honors including a Presidential Commendation and a Merit Award from the National Assembly of the Republic of Korea.
KAIST presented the inauguration as a ceremony marking the start of a new presidency and as a forum for sharing the university's future vision and implementation strategies. The occasion marked KAIST's move beyond "a KAIST that embraces challenges" toward "a KAIST that sets the next standard for innovation," as it pursues its goal of becoming a world-leading university for innovation.
2026.08.10 View 230 -
Twelve Years Later, KAIST’s Undergraduate Research Program Demonstrates Its Lasting Impact on Developing World-Class Talent
KAIST’s undergraduate research programs have helped launch the careers of professors at world-leading universities and experts in global industry in just over a decade. Three students featured as undergraduate researchers in 2014 have since built distinguished careers: two are now professors at leading universities in the United States, while the third works as an open innovation expert at a global pharmaceutical company. Their career paths demonstrate the lasting impact of KAIST’s Undergraduate Research Participation Program (URP) on talent development.
KAIST (President Choongsik Bae) announced on Aug 9 that its Undergraduate Research Participation Program (URP), which enables undergraduate students to formulate their own research questions and experience the entire research process in faculty laboratories, has become a cornerstone of the Institute’s efforts to develop world-class researchers and science and technology professionals.
URP is one of KAIST’s flagship research education programs. It allows undergraduate students to conduct actual research projects in faculty laboratories and directly experience the entire research process, from developing research ideas to conducting experiments, analyzing data, and writing papers. Operated with support from the Ministry of Science and ICT, the program has conducted a total of 679 research projects over the past five years. Through these projects, students have generated a wide range of research outcomes, including publications in international academic journals, patent applications, and awards at international conferences.
“KAIST has steadily expanded research-centered education so that undergraduate students can formulate their own questions and create new knowledge in a world-class research environment,” said President Choongsik Bae. “We will continue to provide strong support through URP and other research programs enabling students to take on challenges without fear of failure and grow into science and technology leaders who drive innovation at universities and in industry around the world.”
A notable example can be found in the laboratory of Professor YongKeun Park in the Department of Physics. In 2014, KAIST highlighted the achievements of undergraduate researchers in Professor Park’s laboratory in an article titled “Professor YongKeun Park Produces Undergraduate Students with International Achievements.” The three students featured at the time have since grown into world-class researchers and professionals, each pursuing a different career in academia or industry.
Sangyeon Cho began working in a laboratory during his first year at KAIST and completed more than 30 credits of research courses by the time he graduated. One of the two first-author papers he published as an undergraduate, his review article on optical imaging techniques for malaria was featured on the cover of Trends in Biotechnology in 2012. He later earned his Ph.D. through the Harvard-MIT Health Sciences and Technology program and served as an assistant professor at Harvard Medical School before joining Rice University as an assistant professor in July 2026. He currently studies technologies that use the world’s smallest nanolasers to track individual cancer cells and therapeutic cells over extended periods.
YoungJu Jo began conducting research combining microscopy and artificial intelligence as an undergraduate, building an interdisciplinary foundation early in his career. His research at the time on virtual staining and diagnosis was published in journals including Nature Cell Biology and Science Advances. He later conducted neuroscience research at Stanford University and published a first-author paper that was featured on the cover of Cell in 2022. In July 2026, Jo joined UC Berkeley as an assistant professor, where he is developing next-generation brain-computer interface (BCI) technologies capable of delivering complex information to the brain.
Seoeun Lee carried the research mindset she developed as an undergraduate into a career in industry. After earning her Ph.D. from Columbia University and working at Boston Consulting Group, she joined global pharmaceutical company Eli Lilly. She currently leads External Innovation activities in the company’s neuroscience division, identifying and pursuing collaborations with promising biotechnology companies through mergers and acquisitions, licensing, partnerships, and other arrangements. Her career demonstrates that undergraduate research experience can lead not only to traditional research careers but also to roles in strategy and collaboration within science- and technology-based industries.
Although the three alumni ultimately pursued careers in different settings—universities and industry—their journeys began in much the same way. During their first or second year as undergraduates, they independently sought out opportunities in laboratories and experienced research that began with questions they were personally curious about rather than merely executing assigned experiments. As an undergraduate, Sangyeon Cho conceived an idea for a super-resolution microscope after seeing a streetlight turn on while walking back to his dormitory late at night. Together with Professor Park, he developed this initial curiosity into a scientific question and ultimately into a research paper.
This undergraduate research culture continues at KAIST today. In 2023, research on GOBI, a methodology for estimating causal relationships in time-series data, involving undergraduate Seho Park as first author, was published in Nature Communications.
In 2024, undergraduate Taesik Youn, serving as first author, conducted the world’s first total synthesis of the natural product securinine G, which has potential applications in cancer treatment and drug development. In 2025, two studies involving undergraduate Minjae Kim were published. His co-first-authored research on a wearable carbon dioxide sensor for real-time breath monitoring appeared in Device, a Cell Press journal, while his lead-author study on OLED displays was published in Nature Communications. Undergraduate Jaehong Cho received both the Best Paper Award and the Distinguished Artifact Award at an IEEE international conference based on his URP research. Through URP, undergraduate-led, world-class research achievements continue to emerge across diverse fields, including drug development, wearable devices, displays, and artificial intelligence. These students are not only publishing in internationally recognized journals and receiving awards at international conferences but also developing advanced research capabilities early in their academic careers.
“These students did not become outstanding researchers through mentorship alone,” said Professor YongKeun Park. “I am grateful that KAIST has created an environment in which faculty members can conduct research alongside such exceptional students. A professor’s role, I believe, is to help students further develop the tremendous potential they already possess.”
“Research is about discovering something new, which means that undergraduate and graduate students begin from the same starting point,” he added. “What ultimately shapes a researcher is the depth of their engagement, their persistence in the face of setbacks, and their ability to formulate questions independently and seek out answers.”Questions first explored in undergraduate laboratories 12 years ago are now driving new research and innovation at universities and companies around the world. KAIST will continue to expand research opportunities through URP so that students can pursue their own questions and create new knowledge.
2026.08.10 View 261 -
KAIST Develops ‘Chameleon AI Semiconductor’ with Programmable Response Speeds
AI semiconductors are becoming more programmable. KAIST researchers have developed a device whose response characteristics can be programmed to process data changing at different speeds. The technology reduced prediction errors for time-varying data by up to 40-fold and is expected to enhance real-time AI performance in autonomous vehicles, robots, and wearable devices.
KAIST (President Choongsik Bae) announced on August 7 that a research team led by Chair Professor Shinhyun Choi from the School of Electrical Engineering and the Graduate School of Semiconductor Technology has developed a programmable dynamic memtransistor (PDM), a semiconductor device whose time-response characteristics can be adjusted to multiple states and retained, as well as an integrated array based on the device.
A memtransistor is a next-generation semiconductor device that combines the information-storage function of memory with the computing function of a transistor. In the developed PDM, the ability to process data while retaining previous information allows its response characteristics to be adjusted and retained for incoming data.
Today’s computers and smartphones require complex software processing to analyze data that changes over time, resulting in large computational loads and high power consumption. To address this, researchers have been studying technologies that allow semiconductor hardware itself to process data directly. However, conventional devices have had fixed response speeds that cannot be changed once the device is fabricated.
The research team overcame this limitation by introducing a dual-layer structure inside the transistor, combining a charge storage layer that accumulates and processes data with an electron trapping layer that controls the response speed in a nonvolatile manner.
In the PDM developed by the research team, incoming data is processed in the charge storage layer, while the electron trapping layer controls, across multiple levels, the recovery speed at which the semiconductor returns to its original state. In experiments, the team succeeded in tuning the current recovery time over an approximately 5-fold range and the characteristic frequency over a range of more than 10-fold.
In particular, in experiments involving the prediction of data in which fast and slow changes are intricately mixed, the PDM reduced prediction errors by as much as 40 times compared with conventional fixed-response semiconductor devices. The PDM enables accurate information processing even when handwriting or object-movement speeds vary, by using response characteristics configured to match different input timescales. Once the response characteristics are set, the device remembers them without requiring a continuous external power supply, and it does not require complex preprocessing of input data. Because it is fully compatible with materials used in widely adopted commercial semiconductor processes, it is also highly advantageous for mass production and commercialization.
The research team fabricated a PDM array and used it to predict complex data, confirming that it achieved accuracy comparable to conventional software-based systems while consuming far less energy.
“This study demonstrates an AI semiconductor whose response characteristics can be programmed to efficiently process data changing at different speeds,” said Chair Professor Choi. “We expect it to become a core technology that improves the performance of AI devices such as autonomous vehicles, robots, and wearables while reducing their power consumption.”
This research was led by KAIST Graduate School of Semiconductor Technology Ph.D. candidate Dae-won Kim as the first author, with Yoonho Cho, Seokho Seo, Yujin Kim, See-On Park, Taehwan Jang, and Chaebin Park participating as co-authors. Young Taek Oh and Fellow Jae-Duk Lee of Samsung Electronics’ Semiconductor R&D Center also participated as co-authors, and Chair Professor Shinhyun Choi served as the corresponding author. The research was published in July in the internationally renowned journal Nature Communications on July 4.
Paper title: Programmable memtransistor array with temporal dynamics modulation for efficient time-series data processing,
DOI: https://doi.org/10.1038/s41467-026-75211-5
This research was supported by the National R&D Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT, the ETRI R&D Support Program of the Institute of Information & Communications Technology Planning & Evaluation, the HRD Program for Industrial Innovation of the Korea Institute for Advancement of Technology funded by the Ministry of Trade, Industry and Energy, Samsung Electronics, and others.
2026.08.07 View 513 -
KAIST Reconstructs Transparent Structures Through Dynamic Scattering Layers in a Single Shot
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.
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.
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.
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).
2026.08.06 View 282 -
KAIST brings ‘giant batteries’ closer to commercialization in the AI data center era
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.
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.
2026.08.05 View 509 -
KAIST and Seoul National University Students Hold 100-Hour Robot Hackathon to Nurture Physical AI Talent
KAIST (President Choongsik Bae) announced on August 4 that RoboticUS, a joint student organization formed by students from KAIST and Seoul National University, is holding the inaugural Robot Hackathon at KAIST from August 3 to 8.
"In the era of Physical AI, we need convergence talent who can go beyond building good AI to design and implement robots and systems that move the real world based on AI," said Choongsik Bae, President of KAIST. "This hackathon, planned and run entirely by students, is a good example of KAIST's culture of challenge and collaboration, and we expect it to become a new educational model for turning future technologies into reality," he added.
The hackathon puts this educational philosophy directly into students' hands. It is Korea's first student-led Physical AI robot hackathon, planned and run by students from KAIST and Seoul National University across institutional boundaries. Participants experience the entire process of designing and building working robots, developing hands-on capabilities that integrate AI and hardware.
The event is hosted by RoboticUS, a nonprofit student organization formed jointly by MR, a robotics club in KAIST's Department of Mechanical Engineering, and Seoul National University's robotics clubs SHAPE and SIGMA. Students who share a passion for robotics from the two universities joined forces across institutional lines, handling every stage themselves — from recruiting participants to designing the mission, running the event, and setting up the presentation and judging format. KAIST's Department of Mechanical Engineering supports the event with facilities and operational assistance so that the students' initiative can translate into genuine educational value.
Ten teams — 30 students total — selected from the two universities will take part. After receiving training in power circuits and robot joint control on August 3 and 4, participants will begin building their robots when the mission is unveiled on the morning of August 5 and continue working until 4 p.m. on August 8. Starting from an idea, they will go through design, assembly, programming, and repeated testing to complete a working robot — experiencing the full roughly 100-hour cycle themselves.
Each team will be provided with Angel Robotics' "phact" actuator, which serves as the robot's joints and muscles, and NVIDIA's Jetson AGX, which functions as the robot's brain. Taejin Technology Co., Ltd will provide training on circuits and electronic components for supplying stable power to the robots, and Angel Robotics will support hands-on training in using the actuators and controlling the robots.
Participants will not simply assemble a finished kit — they will design the robot's shape and movement from the ground up and build it themselves.
For fairness, the mission will be revealed only at the start of the hackathon on August 5. There is no single correct answer or predetermined robot form. Each team will interpret the same mission differently, combining mechanical structure, circuitry, AI, and control software into a single robot. One of the highlights will be seeing the different solutions the ten teams develop in response to the same mission.
The final day, August 8, will be an open Physical AI festival that welcomes the general public. A public conference at the KI Building (E4) Fusion Hall will introduce the current state of Physical AI in an accessible and engaging way — from robotic skin that lets robots feel touch like humans, to humanoid robots that can see and hear people, to a quadrupedal robot that has completed a marathon.
Professors Jung Kim, Yong-Hwa Park, and Jemin Hwangbo of the Department of Mechanical Engineering, along with Joon-Ha Kim, CEO of Diden Robotics, will each give a talk on robotic skin and haptics, multimodal perception in humanoid robots, the quadrupedal robot Raibo, and the journey of developing Physical AI for industrial use, respectively.
After the conference, an open demo day for the ten participating teams will run from 4 to 5 p.m. at KAIST's Culture Complex (E9), 3rd floor. Members of the public will be able to visit each team's booth, watch the robots the students built over 100 hours in action, and submit their own evaluations via QR code.
Judging criteria include mission achievement, technical execution, creativity, and presentation and demonstration. The final score will weight faculty advisor evaluation at 30%, peer evaluation among hackathon participants at 30%, sponsor judging panel evaluation at 30%, and pre-registered public attendee evaluation at 10%.
Angel Robotics and Taejin Technology are taking part as core technology partners, providing equipment and training. Faculty members and industry experts are providing education and technical guidance so that students can safely handle equipment used in real research and industrial settings.
"Physical AI's competitiveness comes not just from software but from hardware and control technology," said Kyoungchul Kong, Professor from Mechanical Engineering at KAIST and Head of the Future Technology Institute at Angel Robotics. "This hackathon will show that with high-performance robot components and the right development environment in place, even undergraduates can turn their imagination into a working robot in 100 hours," he added.
"This hackathon is about learning and building together, rather than competing between schools," said Yeonsu An, President of RoboticUS (and President of MR, KAIST's robotics club). "We hope the general public will get to experience the robots students have built firsthand and take part in the judging, coming away with the sense that Physical AI is a technology anyone can understand and enjoy — not just something for experts," she added.
“Although we do not yet know what the challenge will be, I am most looking forward to gathering in one place and developing robots together,” said Hyeontae Jeon, a participating student from Seoul National University. “It will be even more meaningful to work through challenges across university boundaries and present the robots we built ourselves to the public.”
The public conference is open to anyone through pre-registration. Pre-registered attendees can watch the lectures, view the open demo day, and take part in on-site judging. Registration is available on the RoboticUS official website or on Event-us, under "First Robot Hackathon – Robot & AI Public Conference (KAIST × SNU)." Registration closes August 6.
2026.08.04 View 606 -
KAIST Develops Marine Carbon Removal Technology That Turns Carbon Dioxide in Seawater into “Stone” for Permanent Storage
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.
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.
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.
2026.08.04 View 625 -
KAIST Develops AI That Generates Feasible Plans for Delivery, Production, and Workforce Scheduling
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.
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.
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.
2026.08.03 View 558 -
KAIST Develops a Way to Combat Cancer Cachexia, the Wasting Syndrome That Debilitates Patients
A new path has opened toward stopping cancer-associated cachexia, a devastating complication that gradually wastes patients away. A KAIST research team has developed an RNA-based therapeutic strategy that blocks a brain signal to prevent muscle loss and extend survival.
KAIST (President Choongsik Bae) announced on the 2nd of August that a joint research team led by Professor Minho Shong and Professor Jinkuk Kim from the Graduate School of Medical Science and Engineering, together with the KAIST faculty startup THOR Therapeutics (CEO Minho Shong), identified a new therapeutic principle for cancer cachexia.
Cancer cachexia affects 50 to 80 percent of all cancer patients, making it one of the most common complications of the disease. As cancer cells disrupt the body's metabolism, patients continue to lose weight and muscle mass even when eating sufficiently, leading to severe physical decline. This is a major reason chemotherapy often becomes less effective and treatment is discontinued, ultimately lowering survival rates. Existing drugs, however, have been limited to temporarily boosting appetite and have failed to fundamentally address the underlying muscle loss and metabolic dysfunction.
The research team focused on the idea that the root cause of cancer cachexia lies not in the body, but in the brain. The team noted that when GDF15 (Growth Differentiation Factor 15)—a signaling protein secreted in large amounts as cancer progresses—binds to GFRAL, a receptor protein in the brainstem, it triggers a signal instructing the body to stop eating and instead break down stored muscle and fat, driving progressive physical decline.
To block this process, the team worked with Professor Kim's group to develop a therapeutic using antisense oligonucleotides (ASO)—an RNA-based gene therapy technologies that selectively suppresses the activity of a specific gene—to prevent GFRAL from being produced in the first place.
In effect, the treatment switches off the receiver of the signal that cancer cells send instructing the body to waste away. By blocking GFRAL production at the RNA stage—the intermediate step in which genetic information is converted into protein—the therapy shuts down the cachexia-inducing signal at its source.
The team administered the treatment to mice in which cancer cachexia had already progressed. The result was a substantial reduction in muscle and fat loss, along with the restoration of the metabolic function that had previously broken down. Notably, even though treatment began after the disease had advanced significantly, survival at the study's endpoint (around day 50) was markedly higher in the treated group—90 percent—compared with just 20 percent in the untreated group, demonstrating the therapy's potential for treating cancer cachexia.
Unlike existing treatments that only stimulate appetite, this study is significant in that it blocks the underlying signal driving the wasting process itself. The therapy improved both muscle loss and metabolic dysfunction, and researchers expect that it could eventually be used alongside existing cancer treatments as a next-generation adjuvant therapy to improve patients' quality of life, treatment effectiveness, and survival rates.
"While existing therapies have only temporarily boosted appetite, this study is significant in that it directly targeted a key receptor in the brainstem at the RNA level to suppress the root cause of cancer cachexia," said Professor Song. He added that the team's goal is to move forward with follow-up preclinical research and drug manufacturing as well as quality-control systems without delay, begin clinical development in cancer patients by 2030, and develop the therapy into a treatment that improves patients' quality of life and survival rates.
Dr. Hyunjung Hong from the Graduate School of Medical Science and Engineering, Dr. Minhee Lee from THOR Therapeutics, and Dr. Minsung Park from the Graduate School of Medical Science and Engineering participated as co-first authors, with Professor Song and Professor Kim serving as co-corresponding authors. The findings were published in the international journal Cell Reports Medicine on July 27.
Paper title: Therapeutic Gfral silencing via antisense oligonucleotides ameliorates cancer-associated cachexia and extends survival in tumor-bearing mice
DOI : https://doi.org/10.1016/j.xcrm.2026.102939
This research was supported by the National Research Foundation of Korea, the Korea Health Industry Development Institute, and the Ministry of SMEs and Startups.
2026.08.03 View 384 -
KAIST Develops AI That Avoids Hallucinating Even at Night or in Smoke
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.
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.
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).
2026.08.03 View 513