Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving
Summary: arXiv:2510.00919v3 Announce Type: replace-cross
Abstract
Retrieval-augmented generation (RAG) with foundation models has shown remarkable performance across a variety of tasks; however, their potential for expert-level reasoning, particularly in solving Olympiad-level physics problems, remains largely uncharted. Inspired by the methods students employ to prepare for competitions—specifically, reviewing past problems—this study investigates the capabilities of RAG to enhance physics reasoning within foundation models.
Introduction
The field of artificial intelligence continues to evolve, especially with the advent of foundation models that are capable of executing complex reasoning tasks. Nevertheless, the challenge of applying these models to specialized fields such as physics has been a focal point of research. As students often rely on past Olympiad problems to hone their skills, this study aims to leverage a similar approach through RAG.
Introducing PhoPile
To facilitate this investigation, we introduce PhoPile, an innovative multimodal dataset meticulously designed for Olympiad-level physics. PhoPile serves as a comprehensive resource that enables a systematic study of retrieval-based reasoning in physics problem solving. The dataset encompasses:
- Diagrams
- Graphs
- Equations
This multimodal approach captures the complexity and interconnectivity inherent in physics problem-solving, thereby providing a richer context for models to learn from.
Methodology
Using PhoPile, we benchmark RAG-augmented foundation models, focusing on both large language models (LLMs) and large multimodal models (LMMs). The study employs multiple retrievers to assess the effectiveness of retrieval-augmented generation in enhancing the reasoning capabilities of these models.
Results
The results derived from our benchmarks reveal significant insights:
- Integrating retrieval with physics corpora notably improves model performance, indicating that RAG is a viable strategy for tackling complex physics problems.
- However, we also encountered several challenges that point to the necessity for further research in the domain of retrieval-augmented physics reasoning.
Conclusion
This research highlights the untapped potential of retrieval-augmented generation in enhancing expert-level reasoning within foundation models, particularly in the realm of physics. As we continue to explore the capabilities of RAG and datasets like PhoPile, we pave the way for significant advancements in how AI can assist in solving complex academic challenges.
The findings underscore the importance of developing specialized datasets and methodologies that can effectively bridge the gap between general AI capabilities and the specialized reasoning required for fields such as physics. Future research will undoubtedly benefit from these insights, propelling us toward more sophisticated AI systems that can engage with high-level academic problems.