LOCA-R: Near-Perfect Performance on the Chinese Physics Olympiad 2025

Olympiad-level physics problem-solving presents a significant challenge for both humans and artificial intelligence (AI), as it requires a sophisticated integration of precise calculation, abstract reasoning, and a fundamental grasp of physical principles. The Chinese Physics Olympiad (CPhO), renowned for its complexity and depth, serves as an ideal and rigorous testbed for these advanced capabilities. In this paper, we introduce LOCA-R (LOgical Chain Augmentation for Reasoning), an improved version of the LOCA framework adapted for complex reasoning, and apply it to the CPhO 2025 theory examination. LOCA-R achieves a near-perfect score of 313 out of 320 points, solidly surpassing the highest-scoring human competitor and significantly outperforming all baseline methods. Physics problem-solving, or more generally complex scientific reasoning, stands as a challenging frontier for both humans and AI. It demands not only mathematical derivations, but also the ability to translate complex real-world scenarios described in natural language into abstract models. This process requires a deep understanding of physical laws to select and apply the appropriate principles. Therefore, given the strict demand for accuracy in scientific problems, the most critical task is to maximize the problem-solving capabilities of LLMs, pushing them towards perfect scores. The trade-off between performance and computational cost, while important, is a subsequent consideration.