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April 28, 2025
Artificial intelligence (AI) has made rapid progress in recent years, particularly in the field of large language models (LLMs). These models can generate texts, translate, and answer questions, but their ability to reason logically, especially in complex areas like physics, remains a challenge. A new benchmark called PHYBench aims to test precisely these abilities and reveal the limitations of current LLMs.
PHYBench consists of 500 carefully selected tasks from various areas of physics, including mechanics, electromagnetism, thermodynamics, and optics. The tasks are designed to require a deep understanding of physical concepts and the ability to draw logical conclusions. It's not just about applying memorized formulas, but about analyzing problems and developing step-by-step solutions. The tasks range from simple calculations to complex scenarios that require multi-step reasoning.
Initial tests with PHYBench show that even advanced LLMs like Gemini 2.5 Pro still struggle to solve physics problems. While human participants achieve an accuracy of over 60%, the accuracy of Gemini 2.5 Pro is only 36.9%. These results demonstrate that while LLMs possess impressive language processing capabilities, they are still far from reaching the physical understanding of a human. The discrepancy between human and machine performance underscores the need for further research and development in this area.
PHYBench provides a valuable tool for evaluating and improving LLMs. By providing a standardized test set, researchers can objectively compare the strengths and weaknesses of different models and work specifically on improving their abilities in physical reasoning. The results of PHYBench can help drive the development of new training methods and architectures for LLMs and ultimately lead to more powerful AI systems.
The development of AI systems that can solve physics problems is an important step towards a truly intelligent machine. PHYBench represents a significant milestone in this process by highlighting the current limitations of LLMs and setting the direction for future research. The ability to understand and apply physical concepts is not only relevant for scientific applications, but also for many areas of daily life, from robotics to medicine. The further development of LLMs with physical understanding therefore promises enormous potential for innovation and progress.
Mindverse, a German company, specializes in the development and implementation of AI solutions. As an all-in-one content tool, Mindverse offers a comprehensive platform for AI text, content, images, and research. In addition, Mindverse develops customized solutions such as chatbots, voicebots, AI search engines, and knowledge systems for companies. With its focus on innovation and customer satisfaction, Mindverse positions itself as the ideal partner for companies that want to leverage the potential of AI.
Bibliographie: - https://arxiv.org/pdf/2504.16074 - https://x.com/HuggingPapers/status/1916262867214520640 - https://arxiv.org/abs/2504.16074 - https://x.com/AntonDVP/status/1915839375155282055 - https://www.youtube.com/watch?v=XiipjaITqH8 - https://mbzuai.ac.ae/news/can-llms-reason-new-benchmark-puts-models-to-the-test/