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KAIST, Developing National Positioning Infrastructure with Wi-Fi-Based Precision Technology… A Step Toward “Location Sovereignty”
<(From Left) Prof. Dong-Soo Han, Dr. Kyuho Son, Dr. Byeongcheol Moon, Dr. Sumin Ahn, Ph.D candidate Seungwoo Chae>
A Korean research team has developed a technology that enables precise indoor positioning using only a smartphone. Developed over eight years by KAIST researchers, this technology is expected to help secure critical time in missing-person searches and is being recognized as a “location sovereignty” solution that could reshape the current location service ecosystem dominated by global big tech companies such as Google and Apple.
KAIST (President Kwang Hyung Lee) announced on the 2nd pf April that a research team led by Professor Dongsoo Han of the School of Computing has developed a core technology that can build a nationwide high-precision positioning infrastructure in a short time and at low cost by combining smartphone Wi-Fi signals with real-world address data. This achievement is the result of eight years of research, during which the team filed around ten patents to enhance the technology’s completeness.
The key feature of this technology is its use of Wi-Fi signals collected by smartphones in everyday life. It can provide precise location information anywhere in the country without requiring large-scale equipment or additional infrastructure. It also maintains high accuracy in environments where GPS is weak, such as indoors, underground, or in dense high-rise areas.
In particular, this research is seen as a challenge to the location service ecosystem currently led by global platform companies. Today, most location data worldwide is accumulated and managed by a small number of big tech firms, and Korea also relies heavily on these platforms.
Most importantly, this research establishes a foundation for independently building and managing location data generated domestically. Amid ongoing debates over exporting high-resolution national maps (1:5,000 scale spatial data detailing buildings and roads), the importance of data sovereignty is growing. This technology is drawing attention as an alternative that could reduce dependence on global big tech and realize “location sovereignty.”
The research team proposed a method that automatically combines Wi-Fi signals collected during smartphone app usage with the actual address of the location. This allows the construction of a unique “signal pattern map” (signal fingerprint) for each place, with accuracy improving as more data accumulates.
In a real-world demonstration in Daejeon, using a gas meter reading app, an average of about 30 Wi-Fi signals were detected per household in apartment complexes. This confirmed that city-scale location data can be rapidly built using this approach.
<Status of Radio Map Construction in Daejeon Using a Gas Meter Reader App>
<Address-Based Automation of Wireless Signal Collection and AI-Based Location Labeling Techniques for Collected Wireless Signals>
This technology is expected to significantly reduce location errors—previously up to hundreds of meters—in emergency situations such as missing-person searches, helping secure critical response time. It can also be applied to “location-based authentication,” allowing payments only at specific locations, thereby helping prevent financial crimes such as identity theft or unauthorized remote transactions.
Furthermore, precise location data is a key infrastructure for future AI industries, including autonomous driving, robotics, and logistics. This achievement is expected to enhance competitiveness across these sectors.
<Research Use Image (AI-Generated Image)>
Professor Dongsoo Han stated, “Positioning infrastructure is not just a convenience technology but a core asset directly linked to national data sovereignty,” adding, “It is time for the government, telecom companies, and platform providers to collaborate in building an independent national positioning infrastructure.”
This research was supported by the Ministry of Science and ICT, the National Research Foundation of Korea, the National Fire Agency, and the Korea Evaluation Institute of Industrial Technology (KEIT) (Grant No. RS-2025-02313957).
2026.04.03 View 806 -
AI Blueprints Stolen with a Single Antenna... Countermeasures Also Proposed
< Professor Jun Han >
From smartphone facial recognition to autonomous vehicles, Artificial Intelligence (AI) has long been protected as a "black box." However, a joint research team from KAIST and international institutions has uncovered a new security threat capable of "peeking" at AI blueprints from behind walls. The team also presented corresponding defense technologies. This discovery is expected to be utilized in strengthening AI security across various sectors, including autonomous driving, healthcare, and finance.
On the 31st, Professor Jun Han’s research team from the KAIST School of Computing announced that they, in collaboration with the National University of Singapore (NUS) and Zhejiang University, developed "ModelSpy"—an attack system capable of hijacking AI model structures from a distance using only a small antenna.
This technology works much like a bugging device, capturing and analyzing minute signals emitted while an AI is operational to reconstruct its internal structure. The research team focused on the electromagnetic (EM) waves generated by Graphics Processing Units (GPUs), which handle AI computations.
When an AI performs complex calculations, the GPU emits subtle electromagnetic signals. By analyzing the patterns of these signals, the team successfully restored the layer configurations and detailed parameter settings of the AI model.
Experimental results showed that the structure of AI models could be identified with high accuracy from up to 6 meters away or through walls, across five types of the latest GPUs. Notably, the team estimated the core structure—the layers of the deep learning model—with an accuracy of up to 97.6%.
< AI model structures can be stolen through walls using an antenna hidden in a bag >
This technology is considered a significant security threat because, unlike traditional hacking, it does not require direct server infiltration or malware installation. An attack can be carried out using only a portable antenna small enough to fit in a bag.
Recognizing that this technology could lead to the leakage of a company's core AI assets, the research team also proposed defensive measures, such as electromagnetic interference and computational obfuscation. This is being hailed as a responsible security study that goes beyond demonstrating an attack to suggesting realistic protection methods.
"This research demonstrates that AI systems can be exposed to new types of attacks even in physical environments," said Professor Jun Han. "To protect critical AI infrastructure, such as autonomous driving and national facilities, it is essential to establish 'cyber-physical security' systems that encompass both hardware and software."
< Research Image (AI-generated) >
Professor Jun Han of the KAIST School of Computing participated as a co-corresponding author. The study was presented at the NDSS (Network and Distributed System Security Symposium) 2026, a top-tier academic conference in computer security, where it received the Distinguished Paper Award in recognition of its innovation.
Paper Title: Peering Inside the Black-Box: Long-Range and Scalable Model Architecture Snooping via GPU Electromagnetic Side-Chan
Paper Link: https://www.ndss-symposium.org/ndss-paper/peering-inside-the-black-box-long-range-and-scalable-model-architecture-snooping-via-gpu-electromagnetic-side-channel/
2026.03.31 View 712 -
KAIST Team Wins Grand Prize at Kakao AI Incubation Project
<(From Left) Professor Jongse Park, Professor youngjin Kwon, Professor Jaehyuk Huh, Professor Knunle Olukotun>
Currently, Large Language Model (LLM) services like ChatGPT rely heavily on expensive GPU servers. This structure faces significant limitations, as costs and power consumption skyrocket as service scales increase. Researchers at KAIST have developed a next-generation AI infrastructure technology to address these challenges.
KAIST announced on January 30th that the ‘AnyBridge AI’ team, led by Professor Jongse Park from the School of Computing, has developed a next-generation AI infrastructure software. This software allows for efficient LLM services by integrating various AI accelerators instead of relying solely on GPUs. The technology won the Grand Prize at the "4 ISTs (Science & Tech Institutes) × Kakao AI Incubation Project" hosted by Kakao.
This project is a joint industry-academic collaboration between Kakao and the four major science and technology institutes (KAIST, GIST, DGIST, and UNIST). It selected outstanding teams by evaluating the technical prowess and business viability of preliminary startup teams based on AI technology. The Grand Prize winning team receives a total of 20 million KRW in prize money and up to 35 million KRW in Kakao Cloud credits.
AnyBridge AI is a technical startup team led by Professor Jongse Park (CEO), with Professors Youngjin Kwon and Jaehyuk Huh from KAIST's School of Computing participating. Based on research achievements in AI systems and computer architecture, the team aims to develop technology applicable to actual industrial sites. Furthermore, Professor Kunle Olukotun of Stanford University—co-founder of the Silicon Valley AI semiconductor startup SambaNova—is participating as an advisor to push for global technology and business expansion.
The AnyBridge team noted that most current LLM services are dependent on expensive GPU infrastructure, leading to structural limits where operating costs and power usage surge as services scale. The researchers analyzed that the root cause of this issue lies not in the performance of specific hardware, but in the absence of a system software layer capable of efficiently connecting and operating various AI accelerators, such as NPUs (AI-specialized chips) and PIMs (next-gen chips that process AI within memory), alongside GPUs.
<Technical diagram of AnyBridge: Enhancing LLM performance by flexibly utilizing various AI accelerators>
In response, the AnyBridge team proposed an integrated software stack that can service LLMs across the same interface and runtime environment, regardless of the accelerator type. Specifically, they received high praise for pointing out the limitations of existing GPU-centric LLM serving structures and presenting a "Multi-Accelerator LLM Serving Runtime Software" as their core technology.
This technology enables the implementation of a flexible AI infrastructure where the most suitable AI accelerator can be selected and combined based on the task's characteristics, without being tied to a specific vendor or hardware. This is evaluated as a major advantage that can reduce costs and power consumption while significantly increasing scalability for LLM services.
<Illustration of the Multi-Accelerator LLM Service Platform - AI-generated image>
Additionally, based on years of accumulated research in LLM serving system simulation, the AnyBridge team possesses a research foundation that can pre-verify various hardware/software design combinations without building a large-scale physical infrastructure. This point demonstrated both the technical maturity and the industrial feasibility of their work.
"This award is a result of recognizing the necessity of system software that integrates various AI accelerators, moving beyond the limits of GPU-centric AI infrastructure," said Professor Jongse Park. He added, "It is meaningful that we could expand our research results into industrial fields and entrepreneurship. We will continue to develop this into a core technology for next-generation LLM serving infrastructure through cooperation with industrial partners."
This award is seen as a prime example of KAIST's research moving beyond academic papers toward next-generation AI infrastructure technology and startups. AnyBridge AI plans to advance and verify its technology through future collaborations with Kakao and related industrial partners.
<Photo of the Grand Prize ceremony: Left - Kakao Investment CEO Do-young Kim; Right - KAIST Prof. Jongse Park>
2026.01.30 View 2030 -
AI Enters the Experienced Hire Era... Teaching Learned Knowledge with Ease
< (From left) KAIST Professor Hyunwoo J. Kim, Postdoctoral Researcher Sanghyeok Lee, M.S candidate Taehoon Song, Korea University Ph.D candidate Jihwan Park >
How inconvenient would it be if you had to manually transfer every contact and photo from scratch every time you switched to a new smartphone? Current Artificial Intelligence (AI) models face a similar predicament. Whenever a superior new AI model—such as a new version of ChatGPT—emerges, it has to be retrained with massive amounts of data and at a high cost to acquire specialized knowledge in specific fields. A Korean research team has developed a "knowledge transplantation" technology between AI models that can resolve this inefficiency.
KAIST announced on January 27th that a research team led by Professor Hyunwoo J. Kim from the School of Computing, in collaboration with a research team from Korea University, has developed a new technology capable of effectively "transplanting" learned knowledge between different AI models.
Recently, Vision-Language Models (VLM), which understand both images and text simultaneously, have been evolving rapidly. These are easily understood as multimodal AIs, like ChatGPT, which can provide explanations when a user shows them a photo and asks a question. These models have the advantage of adapting relatively quickly to new fields using small amounts of data by pre-learning large-scale image and language data.
However, the need to repeat this "adaptation process" from scratch every time a new AI model is released has been pointed out as a major inefficiency. Existing adaptation techniques also faced limitations: they were difficult to use if the model structure changed even slightly, or they significantly increased memory and computational costs because multiple models had to be used simultaneously.
To solve these problems, the research team proposed "TransMiter," a transferable adaptation technique that allows learned knowledge to be reused regardless of the model's structure or size. The core of this technology is directly transferring the "adaptation experience" accumulated by one AI as it learns to another AI model.
< TransMiter: A transferable adaptation technique reusable regardless of model structure, size, etc. >
The researchers' technology does not overhaul the complex internal structure of the AI; instead, it adopts a method of passing on "know-how" learned by observing only the prediction results (output) to another AI. Even if the AI models have different architectures, if the know-how learned by one AI is organized based on the answers given to the same questions, another AI can utilize that knowledge immediately. Consequently, there is no need to undergo the complex and time-consuming retraining process, and there is almost no slowdown in speed.
This study is highly significant as it is the first to prove that AI adaptation knowledge—previously considered almost impossible to reuse if model structures or sizes differed—can be precisely transplanted regardless of the model type. This is expected to not only reduce repetitive learning costs but also be utilized as a so-called "knowledge patch" technology that updates Large Language Models (LLMs) in real-time according to specific needs.
Professor Hyunwoo J. Kim explained, "By extending this research, we can significantly reduce the cost of post-training that had to be performed repeatedly whenever a rapidly evolving hyper-scale language model appears. It will enable 'model patches' that easily add expertise in specific fields."
The study involved Taehoon Song (Master's student, KAIST School of Computing), Sanghyeok Lee (Postdoctoral researcher), and Jihwan Park (Doctoral student, Korea University) as co-authors, with Professor Hyunwoo J. Kim serving as the corresponding author. The research results were accepted for oral presentation (4.6% acceptance rate as of 2025) at AAAI 2026 (Association for the Advancement of Artificial Intelligence), the most prestigious international conference in the field of AI, and were presented on January 25th.
Paper Title: Transferable Model-agnostic Vision-Language Model Adaptation for Efficient Weak-to-Strong Generalization
DOI: https://doi.org/10.48550/arXiv.2508.08604
Meanwhile, Professor Hyunwoo J. Kim's laboratory presented a total of three papers at the conference, including this paper and "TabFlash," a technology developed in collaboration with Google Cloud AI to enhance the understanding of tables within documents.
2026.01.27 View 2091 -
Professor Insik Shin Becomes First Korean to Win the RTSS Most Influential Paper Award
< KAIST Professor Insik Shin >
KAIST announced on December 21st that Professor Insik Shin from the School of Computing has received the Influential Paper Award 2025 at the IEEE Real-Time Systems Symposium (RTSS), the world's most prestigious international conference in the field of real-time systems.
This honor is a "Test of Time Award," presented to papers that have exerted a sustained and significant influence on both academia and industry for more than 10 years after publication. This marks the first time a Korean researcher has received this prestigious award. The ceremony took place at IEEE RTSS 2025 in Boston, USA, on December 4th (local time).
Professor Shin’s award-winning research is the "Periodic Resource Model," co published in 2003 with Professor Insup Lee of the University of Pennsylvania. Rather than trying to verify a complex machine or system all at once, this study developed a method to verify individual components—much like LEGO blocks—to ensure each meets its designated timing requirements. It mathematically guarantees that when these components are assembled, the entire system will operate safely.
Paper Title: Periodic Resource Model for Compositional Real-Time Guarantees
DOI: 10.1109/REAL.2003.1253249
Thanks to this research, it has become possible to design real-time systems that cannot tolerate even a moment of delay—such as autonomous vehicles, aircraft, and industrial robots—with greater precision and safety. This breakthrough overcame the limitations of existing methods that required analyzing an entire system at once, which had become nearly impossible as the complexity of modern real-time systems increased rapidly.
Professor Shin presented a method to divide a system into small modules, verify that each module satisfies its time constraints, and mathematically prove that the safety of the entire system is guaranteed upon integration. This work is credited with establishing the foundation for modern compositional real-time scheduling theory.
At the time of its initial publication in 2003, this paper won the 'Best Paper Award' at RTSS—another first for a Korean researcher. Now, 20 years later, its academic and industrial value has been officially recognized once again. This is because the theory has transcended academic boundaries to become a core analytical tool in various safety-critical industries, including autonomous driving, aerospace control, and industrial automation.
The IEEE Technical Committee stated, "This model has established itself as a core language for modern real-time system design and has guided the direction of research and industry for the past 20 years." The paper is currently featured in textbooks at major universities in the United States and Europe, serving as a standard theory in the field.
"As a scholar, this is the award I have wanted most in my life," said Professor Shin. "I am honored to have it recognized that research from 20 years ago has truly had a major impact on the world. This was made possible by the many researchers and companies who applied this theory to actual systems."
Meanwhile, Professor Shin is expanding his research beyond real-time systems into the field of Artificial Intelligence (AI). He founded the faculty-led startup Fluiz and developed FluidGPT, a mobile AI agent technology that allows users to execute smartphone apps via voice commands. This technology recently won the AI Champion Competition hosted by the Ministry of Science and ICT. Experts evaluate Professor Shin as achieving rare success by bridging basic theory and applied technology, effectively linking research to industry.
2025.12.22 View 1536 -
Where did this fish come from? Securing World-Class Seafood Traceability Technology
< (From left) KAIST Ph.D. candidate Hyeontaek Hwang, Research Professor Yalew Kidane, Senior Researcher Young-jong Lee, Researcher Geon-woo Park, and (Top) Professor Daeyoung Kim >
When buying seafood at a supermarket, you may have wondered where the fish was caught and what process it went through to reach your dinner table. However, due to complex distribution processes, it has been difficult to transparently track that path. KAIST’s research team has developed a digital technology that solves this problem, allowing the movement path of seafood to be checked at a glance based on international standards recognized worldwide.
KAIST announced on December 19th that "OLIOPASS," a GS1 international standard-based digital transformation solution developed by Director Daeyoung Kim (Professor, School of Computing) of the KAIST Auto-ID Labs Busan Innovation Center, has passed the rigorous performance verification of the GDST (Global Dialogue on Seafood Traceability). It is the first in Korea to obtain the "GDST Capable Solution" certification.
< (Left) GDST Global Certification Logo, (Right) KAIST OLIOPASS Platform Logo >
Only 13 technologies worldwide have received this GDST certification. Among them, only 7 entities, including KAIST, support "Full Chain" traceability technology, which manages the entire process from production and processing to distribution and sales.
The GDST is an international organization established in 2015 at the suggestion of the World Economic Forum (WEF). It helps record and share information on all seafood movement processes digitally, according to the GS1 international standard agreed upon by the global community. This can be compared to creating a "common language for the supply chain" used worldwide.
The GDST is a global standard system that increases the reliability of seafood history information by defining Key Data Elements (KDEs) that must be recorded during the movement of seafood and Critical Tracking Events (CTEs) that define when, where, and what moved, based on international standards.
As major food distribution companies in the United States and Europe have recently begun requiring GDST compliance, this standard is becoming a de facto essential requirement for entering the global market. Since 2019, KAIST has participated as a founding member of GDST and has played a key role in designing seafood traceability models and system-to-system information interoperability.
In particular, with the U.S. Food and Drug Administration (FDA) announcing the mandatory enforcement of food traceability (FSMA 204) starting in July 2028, this certification is significant as it secures a technical solution for domestic companies to meet global market regulations.
OLIOPASS, which received certification on November 5th, is a digital traceability platform that combines KAIST's IoT technology with international standards (GS1 EPCIS 2.0, GS1 Digital Link). It records and shares movement information of various products and assets in a standardized language and utilizes blockchain technology to fundamentally prevent forgery or alteration. Even if systems differ between companies, history data is seamlessly linked.
Furthermore, OLIOPASS is designed as an "AI-ready data" infrastructure, allowing for the easy application of next-generation AI technologies such as Large Multimodal Models (LMM), AI agents, knowledge graphs, and ontologies. This allows it to serve as a platform that supports both digital and AI transformation beyond simple history management.
Daeyoung Kim, Director of the KAIST Auto-ID Labs Busan Innovation Center, stated, "This certification is an international recognition of our capability in reliable data technology across the global supply chain. We will expand OLIOPASS beyond seafood and food into various fields such as pharmaceuticals, logistics, defense, and smart cities, ensuring KAIST’s technology grows into a platform used by the world."
※ Related Link: https://thegdst.org/verified-gdst-capable-solutions/
< List of Certified Organizations >
2025.12.22 View 1347 -
AI Technology World No. 1 in Finding the Exact Moment in a Video: Where is the First Place?
< (From left) Professor Joon Hyuk Noh (Assistant Professor, Department of Artificial Intelligence, Ewha Womans University), Seojin Hwan, Yoonki Cho (Ph.D. Candidate), Professor Sung-Eui Yoon (School of Computing, KAIST) >
When faced with a complex question like 'What object disappeared while the camera was pointing elsewhere?', a common problem is that AI often relies on language patterns to guess a 'plausible answer,' instead of actually observing the real situation in the video. To overcome this limitation, our university's research team developed a technology that enables the AI to autonomously identify the 'exact critical moment (Trigger moment)' within the video, and the team’s excellence was proven by winning an international AI competition with this technology. The university announced on the 28th that the research team led by Professor Sung-Eui Yoon from the School of Computing, in collaboration with Professor Joon Hyuk Noh's team from Ewha Womans University, took 1st place in the Grounded Video Question Answering track of the Perception Test Challenge held at ICCV 2025, a world-renowned computer vision conference. The Perception Test Challenge held at ICCV 2025 was organized by Google DeepMind with a total prize pool of 50,000 Euros (approximately 83 million KRW). It assesses the cognitive and reasoning abilities of multimodal AI, which must comprehensively understand various data, including video, audio, and text. Crucially, the core evaluation factor is the ability to make judgments based on actual video evidence, moving beyond language-centric bias. Unlike conventional methods that analyze the entire video indiscriminately, our university's research team developed a new technology that instructs the AI to first locate the core scene (Trigger moment) essential for finding the correct answer. Simply put, this technology is designed to make the AI autonomously determine: “This scene is decisive for answering this question!” The research team calls this framework CORTEX (Chain-of-Reasoning for Trigger Moment Extraction). The research team's system consists of a three-stage structure where three models performing different functions operate sequentially. First, the Reasoning AI (Gemini 2.5 Pro) reasons about which moment is required to answer the question and finds candidate Trigger moments. Next, the Object Location Finding Model (Grounding Model, Molmo-7B) accurately identifies the exact location (coordinates) of people, cars, and objects on the screen during the selected moment. Finally, the Tracking Model (SAM2) precisely tracks the movement of objects in the time frame before and after the selected scene, using that scene as a reference, thereby reducing errors. In short, the 'method of accurately pinpointing a key scene and tracking the evidence for the answer centered on that scene' significantly reduced problems like initial misjudgment or occlusion in the video. In the Grounded Video Question Answering (Grounded VideoQA) track, which saw 23 participating teams, the KAIST team SGVR Lab (Scalable Graphics, Vision & Robotics Lab) recorded 0.4968 points in the HOTA (Higher Order Tracking Accuracy) metric, overwhelmingly surpassing the 2nd place score of 0.4304 from Columbia University, USA, to secure 1st place. This achievement is nearly double the previous year's winning score of 0.2704 points. This technology has wide-ranging applications in real-life settings. Autonomous driving vehicles can accurately identify moments of potential accident risk, robots can understand the surrounding environment smarter, security and surveillance systems can rapidly locate critical scenes, and media analysis can precisely track the actions of people or objects in chronological order. This is a core technology that enables AI to judge based on "actual evidence in the video." The ability to accurately pinpoint how objects behave over time in a video is expected to greatly expand the application of AI in real-world scenarios in the future.
< Pipeline image of the grounding framework for video question answering proposed by the research team >
This research was presented on October 19th at ICCV 2025, the 3rd Perception Test Challenge conference. The achievement was supported by the Ministry of Science and ICT's Basic Research Program (Mid-Career Researcher), the SW Star Lab Project's 'Development of Perception, Action, and Interaction Algorithms for Open-World Robot Services,' and the AGI Project's 'Reality Construction and Bi-directional Capability Approach based on Cognitive Agents for Embodied AGI' tasks."
2025.12.01 View 2257 -
Professor Youngjin Kwon's Team Wins Google Award 'Catches Bugs Without a Real CPU
< Professor Youngjin Kwon >
Modern CPUs have complex structures, and in the process of handling multiple tasks simultaneously, an order-scrambling error known as a 'concurrency bug' can occur. Although this can lead to security issues, these bugs were extremely difficult to detect using conventional methods. Our university's research team has developed a world-first-level technology to automatically detect these bugs by precisely reproducing the internal operation of the CPU in a virtual environment without needing a physical chip. Through this, they successfully found and fixed 11 new bugs in the latest Linux kernel.
Our university announced on the 21st that the research team led by Professor Youngjin Kwon of the School of Computing has won the 'Research Scholar Award' (Systems category) presented by Google.
The Google Research Scholar Award is a global research support program, implemented since 2020, to support Early-Career Professors conducting innovative research in various fields such as AI, Systems, Security, and Data Management.
It is known as a highly competitive program, with the selection process conducted directly by Google Research scientists, and only a tiny fraction of the hundreds of applicants worldwide are chosen. In particular, this award is recognized as one of the most prestigious industry research support programs globally in the field of AI and Computer Systems, and domestic recipients are rare.
■ Technology Developed to Detect Concurrency Bugs in the Latest Apple M3 and ARM Servers
Professor Kwon's team developed a technology that automatically detects concurrency bugs in the latest ARM (a CPU design method that uses less power and is highly efficient) based servers, such as the Apple M3 (Apple's latest-generation computer processor chip).
A concurrency bug is an error that occurs when the order of operations gets mixed up while the CPU handles multiple tasks simultaneously. This is a severe security vulnerability that can cause the computer to suddenly freeze or become a pathway for hackers to attack the system. However, these errors were extremely difficult to find with existing testing methods alone.
■ Automatically Detects Bugs by Reproducing CPU Internal Operations Without a Real CPU
The core achievement of Professor Kwon's team is the 'technology to reproduce the internal operation of the CPU exactly in a virtual environment without a physical chip.' Using this technology, it is possible to precisely analyze the order in which instructions are executed and where problems occur using only software, without having to disassemble the CPU or use the actual chip.
By running the Linux operating system based on this system to automatically detect bugs, the research team discovered 11 new bugs in the latest Linux kernel* and reported them to the developer community, where they were all fixed.
*Linux kernel: The core operating system engine that forms the basis of servers, supercomputers, and smartphones (Android) worldwide. It acts as the 'heart' of the system, managing the CPU, memory, and storage devices.
Google recognized this technology as 'very important for its own infrastructure' and conferred the Award.
< Google Scholar Award Recipient Page >
This technology is evaluated to have general applicability, not only to Linux but also to various operating systems such as Android and Windows. The research team has released the software as open-source (GitHub) so that anyone in academia or industry can utilize it.
Professor Youngjin Kwon stated, "This award validates the international competitiveness of KAIST's systems research," and "We will continue our research to establish a safe and highly reliable computing environment."
※ Google Scholar Award Recipient Page: https://research.google/programs-and-events/research-scholar-program/recipients/ GitHub (Technology Open-Source): https://github.com/casys-kaist/ozz
2025.11.21 View 2283 -
Automatic C to Rust Translation Technology Gains Global Attention for Accuracy Beyond AI
<(From Left) Professor Sukyoung Ryu, Researcher Jaemin Hong>
As the C language, which forms the basis of critical global software like operating systems, faces security limitations, KAIST's research team is pioneering core original technology research for the accurate automatic conversion to Rust to replace it. By proving the mathematical correctness of the conversion, a limitation of existing Artificial Intelligence (LLM) methods, and solving C language security issues through automatic conversion to Rust, they presented a new direction and vision for future software security research. This work has been selected as the cover story for CACM, the world's highest-authority academic journal, thereby demonstrating KAIST's global research leadership in the field of computer science.
KAIST announced on the 9th of November that the paper by Professor Sukyoung Ryu's research team (Programming Language Research Group) from the School of Computing was selected as the cover story for the November issue of CACM (Communications of the ACM), the highest authority academic journal published by ACM (Association for Computing Machinery), the world's largest computer society.
<Photo of the Paper Selected for the Cover of Communications of the ACM>
This paper comprehensively addresses the technology developed by Professor Sukyoung Ryu's research team for the automatic conversion of C language to Rust, and it received high acclaim from the international research community for presenting the technical vision and academic direction this research should pursue in the future.
The C language has been widely used in the industry since the 1970s, but its structural limitations have continuously caused severe bugs and security vulnerabilities. Rust, on the other hand, is a secure programming language developed since 2015, used in the development of operating systems and web browsers, and has the characteristic of being able to detect and prevent bugs before program execution.
The US White House recommended discontinuing the use of C language in a technology report released in February 2024, and the Defense Advanced Research Projects Agency (DARPA) also explicitly stated that Rust is the core alternative for resolving C language security issues by promoting a project to develop technology for the automatic conversion of C code to Rust.
Professor Sukyoung Ryu's research team proactively raised the issues of C language safety and the importance of automatic conversion even before these movements began in earnest, and they have continuously developed core related technologies.
In May 2023, the research team presented the Mutex conversion technology (necessary for program synchronization) at ICSE (International Conference on Software Eng), the top authority conference in software engineering. In June 2024, they presented the Output Parameter conversion technology (used for result delivery) at PLDI (Programming Language Design and Implementation), the top conference in programming languages, and in October of the same year, they presented the Union conversion technology (for storing diverse data together) at ASE (Automated Software Eng), the representative conference in software automation.
These three studies are all "world-first" achievements presented at top-tier international academic conferences, successfully implementing automatic conversion technology for each feature with high completeness.
Since 2023, the research team has consistently published papers in CACM every year, establishing themselves as global leading researchers who consistently solve important and challenging problems worldwide.
This paper was published in CACM (Communications of the ACM) on October 24, with Dr. Jaemin Hong (Postdoctoral Research Fellow at KAIST Information and Electronics Research Institute) as the first author. ※Paper Title: Automatically Translating C to Rust, DOI: https://doi.org/10.1145/3737696
Dr. Jaemin Hong stated, "The conversion technology we developed is an original technology based on programming language theory, and its biggest strength is that we can logically prove the 'correctness' of the conversion." He added, "While most research relies on Large Language Models (LLMs), our technology can mathematically guarantee the correctness of the conversion."
Dr. Hong is scheduled to be appointed as an Assistant Professor in the Computer Science Department at UNIST starting in March 2025.
Furthermore, Professor Ryu's research team has four papers accepted for presentation at ASE 2025, the highest-authority conference in software engineering, including C→Rust conversion technology.
These papers, in addition to automatic conversion technology, cover various cutting-edge software engineering fields and are receiving high international acclaim. They include: technology to verify whether quantum computer programs operate correctly, 'WEST' technology that automatically checks the correctness of WebAssembly programs (technology for fast and efficient program execution on the web) and creates tests for them, and technology that automatically simplifies complex WebAssembly code to quickly find errors. Among these, the WEST paper received the Distinguished Paper Award.
This research was supported by the Leading Research Center/Mid-career Researcher Support Program of the National Research Foundation of Korea, the Institute of Information & Communications Technology Planning & Evaluation (IITP), and Samsung Electronics.
2025.11.10 View 6926 -
KAIST's 'FluidGPT' Wins Grand Prize at the 2025 AI Champion Competition
<Commemorative Photo After Winning at the 2025 AI Champions Award Ceremony>
The era has begun where an AI assistant goes beyond simple conversation to directly view the screen, make decisions, and complete tasks such as hailing a taxi or booking an SRT ticket.
KAIST (President Kwang Hyung Lee) announced on the 6th that the AutoPhone Team (Fluidez, KAIST, Korea University, Sungkyunkwan University), led by Professor Insik Shin (CEO of Fluidez Co., Ltd.) of the School of Computing, was selected as the inaugural AI Champion (1st place) in the '2025 Artificial Intelligence Champion (AI Champion) Competition,' hosted by the Ministry of Science and ICT.
This competition is the nation's largest AI technology contest, comprehensively evaluating the innovativeness, social impact, and commercial potential of AI technology. With 630 teams participating nationwide, the AutoPhone Team claimed the top honor and will receive 3 billion Korean won in research and development funding.
The technology developed by the AutoPhone Team, 'FluidGPT,' is a fully autonomous AI agent that understands a user's voice command and enables the smartphone to independently run apps, click, input, and even complete payments.
For example, when a user says, "Book an SRT ticket from Seoul Station to Busan," or "Call a taxi," FluidGPT opens the actual app and sequentially performs the necessary steps to complete the request.
The core of this technology is its 'Non-Invasive (API-Free)' structure. Previously, calling a taxi using an app required directly connecting to the app's internal system (API communication) through the taxi app's API. In contrast, this technology does not modify the existing app's code or link an API. Instead, the AI directly recognizes and operates the screen (UI), acquiring the ability to use the smartphone just like a human.
As a result, FluidGPT presents a new paradigm—"AI that sees, judges, and moves a hand on behalf of a person"—and is evaluated as a core technology that will usher in the 'AI Phone Era.'
FluidGPT moves beyond simple voice assistance to implement the concept of 'Agentic AI' (Action-Oriented Artificial Intelligence), where the AI directly views the screen, makes decisions, and takes action. As a fully action-oriented system, the AI clicks app buttons, fills in input fields, and references data to autonomously achieve the user's objective, foreshadowing an innovation in how smartphones are used.
Professor In-sik Shin of the School of Computing shared his thoughts, stating, "AI is now evolving from conversation to action. FluidGPT is a technology that understands the user's words and autonomously executes actual apps, and it will be the starting point of the 'AI Phone Era.' The AutoPhone Team possesses world-class research capabilities, and we will contribute to the widespread adoption of AI services that everyone can easily use."
KAIST President Kwang Hyung Lee remarked, "This achievement is a representative example that demonstrates KAIST's vision for AI convergence," adding, "AI technology is entering the daily lives of citizens and leading a new wave of innovation." He further added, "KAIST will continue to lead research in future core technologies such as AI and semiconductors to bolster national competitiveness."
2025.11.06 View 3258 -
KAIST, Dancing Like 'Navillera'... AI Understands and Renders Garment Motions of Avatars
<(From Left)Ph.D candidate Jihyun Lee, Professor Tae-Kyun Kim, M.S candidate Changmin Lee>
The era has begun where AI moves beyond merely 'plausibly drawing' to understanding even why clothes flutter and wrinkles form. A KAIST research team has developed a new generative AI that learns movement and interaction in 3D space following physical laws. This technology, which overcomes the limitations of existing 2D-based video AI, is expected to enhance the realism of avatars in films, the metaverse, and games, and significantly reduce the need for motion capture or manual 3D graphics work.
KAIST (President Kwang Hyung Lee) announced on the 22nd that the research team of Professor Tae-Kyun (T-K) Kim from the School of Computing has developed 'MPMAvatar,' a spatial and physics-based generative AI model that overcomes the limitations of existing 2D pixel-based video generation technology.
To solve the problems of conventional 2D technology, the research team proposed a new method that reconstructs multi-view images into 3D space using Gaussian Splatting and combines it with the Material Point Method (MPM), a physics simulation technique.
In other words, the AI was trained to learn physical laws on its own by stereoscopically reconstructing videos taken from multiple viewpoints and allowing objects within that space to move and interact as if they were in real physical world.
This enables the AI to compute the movement based on objects' material, shape, and external forces, and then learn the physical laws by comparing the results with actual videos.
The research team represented the 3D space using point-units, and by applying both Gaussian and MPM to each point, they simultaneously achieved physically natural movement and realistic video rendering.
That is, they divided the 3D space into numerous small points, making each point move and deform like a real object, thereby realizing natural video that is nearly indistinguishable from reality.
In particular, to precisely express the interaction of thin and complex objects like clothing, they calculated both the object's surface (mesh) and its particle-unit structure (point), and utilized the Material Point Method (MPM), which calculates the object's movement and deformation in 3D space according to physical laws.
Furthermore, they developed a new collision handling technology to realistically reproduce scenes where clothes or objects move and collide with each other in multiple spots and complex manner.
The generative AI model MPMAvatar, to which this technology is applied, successfully reproduced the realistic movement and interaction of a person wearing loose clothing, and also succeeded in 'Zero-shot' generation, where the AI processes data it has never seen during the learning process by inferring on its own.
<Figure 1. Modeling new human poses and clothing dynamics from multi-view video input, and zero-shot generation of novel physical interactions.>
The proposed method is applicable to various physical properties, such as rigid bodies, deformable objects, and fluids, allowing it to be used not only for avatars but also for the generation of general complex scenes.
<“Figure 2. Depiction of graceful dance movements and soft clothing folds, like Navillera.>
Professor Tae-Kyun (T-K) Kim explained, "This technology goes beyond AI simply drawing a picture; it makes the AI understand 'why' the world in front of it looks the way it does. This research demonstrates the potential of 'Physical AI' that understands and predicts physical laws, marking an important turning point toward AGI (Artificial General Intelligence)." He added, "It is expected to be practically applied across the broaden immersive content industry, including virtual production, films, short-form contents, and adverts, creating significant change."
The research team is currently expanding this technology to develop a model that can generate physically consistent 3D videos simply from a user's text input.
This research involved Changmin Lee, a Master's student at the KAIST Graduate School of AI, as the first author, and Jihyun Lee, a Ph.D. student at the KAIST School of Computing, as a co-author. The research results will be presented at NeurIPS, the most prestigious international academic conference in the field of AI, on December 2nd, and the program code is to be fully released.
· Paper: C. Lee, J. Lee, T-K. Kim, MPMAvatar: Learning 3D Gaussian Avatars with Accurate and Robust Physics-Based Dynamics, Proc. of Thirty-Ninth Annual Conf. on Neural Information Processing Systems (NeurIPS), San Diego, US, 2025
· arXiv version: https://arxiv.org/abs/2510.01619
· Related Project Site: https://kaistchangmin.github.io/MPMAvatar/
· Related video links showing the 'Navillera'-like dancing drawn by AI:
o https://www.youtube.com/shorts/ZE2KoRvUF5c
o https://youtu.be/ytrKDNqACqM
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT) through the Human-Oriented Next-Generation Challenging AGI Technology Project (RS-2025-25443318) and the Professional AI Talent Development Program for Multimodal AI Agents (RS-2025-25441313).
2025.10.22 View 3880 -
Refrigerator Use Increases with Stress, IoT Sensors Read Mental Health
<(From Left) Ph.D candidate Chanhee Lee, Professor Uichin Lee, Professor Hyunsoo Lee, Ph.D candidate Youngji Koh from School of Computing>
The number of single-person households in South Korea has exceeded 8 million, accounting for 36% of the total, marking an all-time high. A Seoul Metropolitan Government survey found that 62% of single-person households experience 'loneliness', deepening feelings of isolation and mental health issues. KAIST researchers have gone beyond the limitations of smartphones and wearables, utilizing in-home IoT data to reveal that a disruption in daily rhythm is a key indicator of worsening mental health. This research is expected to lay the foundation for developing personalized mental healthcare management systems.
KAIST (President Kwang Hyung Lee) announced on the 21st of October that a research team led by Professor Uichin Lee from the School of Computing has demonstrated the possibility of accurately tracking an individual's mental health status using in-home Internet of Things (IoT) sensor data.
Consistent self-monitoring is important for mental health management, but existing smartphone- or wearable-based tracking methods have the limitation of data loss when the user is not wearing or carrying the device inside the home.
The research team therefore focused on in-home environmental data. A 4-week pilot study was conducted on 20 young single-person households, installing appliances, sleep mats, motion sensors, and other devices to collect IoT data, which was then analyzed along with smartphone and wearable data.
The results confirmed that utilizing IoT data alongside existing methods allows for a significantly more accurate capture of changes in mental health. For instance, reduced sleep time was closely linked to increased levels of depression, anxiety, and stress, and increased indoor temperature also showed a correlation with anxiety and depression.
<Picture1. Heatmap of the Correlation Between Each User’s Mental Health Status and Sensor Data>
Participants' behavioral patterns varied, including a 'binge-eating type' with increased refrigerator use during stress and a 'lethargic type' with a sharp decrease in activity. However, a common trend clearly emerged: mental health deteriorated as daily routines became more irregular.
Variability in daily patterns was confirmed to be a more important factor than the frequency of specific behaviors, suggesting that a regular routine is essential for maintaining mental health.
When research participants viewed their life data through visualization software, they generally perceived the data as being genuinely helpful in understanding their mental health, rather than expressing concern about privacy invasion. This significantly enhanced the research acceptance and satisfaction with participation.
<Figure 2. Comparison of Average Mental Health Status Between the High Irregularity Group (Red) and the Low Irregularity Group (Blue)>
Professor Uichin Lee stated, "This research demonstrates that in-home IoT data can serve as an important clue for understanding mental health within the context of an individual's daily life," and added, "We plan to further develop this into a remote healthcare system that can predict individual lifestyle patterns and provide personalized coaching using AI."
Youngji Koh, a Ph.D candidate, participated as the first author in this research. The findings were published in the September issue of the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, a prominent international journal in the field of human-computer interaction (HCI). ※ Harnessing Home IoT for Self-tracking Emotional Wellbeing: Behavioral Patterns, Self-Reflection, and Privacy Concerns DOI: https://dl.acm.org/doi/10.1145/3749485 ※ Youngji Koh (KAIST, 1st author), Chanhee Lee (KAIST, 2nd author), Eunki Joung (KAIST, 3rd author), Hyunsoo Lee (KAIST, corresponding author), Uichin Lee (KAIST, corresponding author)
This research was conducted with support from the LG Electronics-KAIST Digital Healthcare Research Center and the National Research Foundation of Korea, funded by the government (Ministry of Science and ICT).
2025.10.21 View 3001