< ICML 2026 Winners Photo. From left: Byeonghu Na (KAIST KI Robotics Institute); Jiseok Kwak (Ph.D. Student, Graduate School of Data Science); Suhyeon Jo (Ph.D. Student, Department of Industrial & Systems Engineering); TaeWoo Kim (Master >
A KAIST research team has won an international AI challenge held in conjunction with ICML 2026 for developing a system that can interpret images and text, apply physical laws, and explain its reasoning. The result demonstrates KAIST's world-leading capabilities in AI for understanding the physical world, with potential applications in aircraft design, robotics, autonomous vehicles, and space systems.
KAIST (President Choongsik Bae) announced on July 23 that a team led by Professor Il-Chul Moon from the KAIST AX Department won the Visual Grounded Physics Problem Solving Challenge, organized by the AI4Math Workshop, an official workshop of the International Conference on Machine Learning (ICML) 2026.
The competition ran from May 1 to June 16 and featured 139 teams, including participants from ETH Zurich, Fudan University, and the Shanghai Innovation Institute. The KAIST team achieved the highest score in the final evaluation and received the top prize at the ICML award ceremony held in Seoul on July 11.
The challenge evaluated whether AI could understand physics problems presented through images and text, apply physical laws, and generate both correct answers and the reasoning process. Participating systems had to integrate multimodal information and use principles such as Newtonian mechanics to solve problems logically. The focus was not simple calculation, but the ability to understand and explain complex physical phenomena.
The achievement suggests that AI can move beyond solving test questions to understanding real physical environments and optimizing their design and operation. The technology could support aircraft, robotics, autonomous driving, and space systems, where decisions must account for real-world physical conditions.
Five researchers from KAIST's Applied Artificial Intelligence Laboratory (AAILab) participated, including doctoral student Jiseok Kwak from the Department of Industrial and Systems Engineering. The team built a multi-agent AI architecture in which several foundation models operated as independent agents and verified and debated one another's answers.
< Example Physics Competition Problem from AI4Math Track 3 >
This approach reduced errors from individual models and improved reasoning reliability, enabling the team to achieve the competition's best performance. It also demonstrated new possibilities for AI reasoning concerning complex physical phenomena and agentic AI systems.
"By transforming foundation models into agents and observing multiple agents reach correct answers through debate and verification, we realized that no single foundation model will monopolize the future," said Professor Moon.
"Although this competition took the form of a physics problem-solving test, the underlying technology is about developing AI for the optimal design and operation of physical systems, such as AI-based aircraft design and operations being explored by Boeing," he added.
The research was supported by the Institute of Information & Communications Technology Planning & Evaluation and the ITRC Defense Swarm Systems Research Center.
