AI Mathematical Olympiad Solver
DOI:
https://doi.org/10.63503/j.ijssic.2025.194Keywords:
Large Language Models, Ensemble AI, Mathematical Reasoning, Consensus Mechanism, Symbolic Verification, Trust Evaluation, Multi-Model OrchestrationAbstract
This research presents AIMOS (AI- Powered Intelligent Math Operations System) — a multi- LLM orchestration framework to improve the correctness and reliability of mathematical problem- solving tasks. The framework annotates input questions to a category of mathematical discourse — Algebra, Calculus, Geometry, Trigonometry, or Probability & Statistics, then routes the questions to multiple large language model (LLM) APIs (including GPT-4, Claude, Gemini and Mistral) and levers their responses. The consensus algorithm and the trust-based algorithm follow to select the final response output with further symbolic verification of mathematical correctness. This experimental framework demonstrates that AIMOS outperforms use of single LLMs, with respect to correctness, reliability, and interpretability, and can be used for educational, research, and analytical purposes.
References
[1] J. Abukhait, M. Alaqtash, A. Aljaafreh, and W. Othman, “Automated detection and classification of soccer field objects using YOLOv7 and computer vision techniques,” International Journal of Advanced Computer Science and Applications, vol. 14, 2023, doi: 10.14569/IJACSA.2023.0141191.
[2] J. Eichner, J. Nowak, B. Grzelak, T. Gorecki, T. Pilka, and K. Dyczkowski, “Advanced vision techniques in soccer match analysis: From detection to classification,” 2024.
[3] S. M. Melasagare, J. S. Shile, S. J. Tipe, and A. R. Ballal, “Football match analysis using YOLO,” Journal of Emerging Technologies and Innovative Research (JETIR), vol. 11, no. 12, 2024.
[4] D. T. L. Divya and T. Yignyen, “Football game analysis and tracking position,” International Journal of Scientific Research & Engineering Trends, vol. 10, no. 5, 2024.
[5] P. P. Dharmamoni, K. N. Sri, K. Pradnya, and D. Shravani, “Real-time football match analysis using deep learning,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 6, no. 2, 2025.
[6] B. M. Merzah, M. S. Croock, and A. N. Rashid, “Football player tracking and performance analysis using the OpenCV library,” Mathematical Modelling of Engineering Problems, vol. 11, no. 1, pp. 123–132, 2024.
[7] G. Kaur, H. Maheshwari, D. Uppal, and A. P. Rai, “Advanced football analysis system using machine learning, computer vision, and deep learning techniques,” International Research Journal of Modernization in Engineering Technology and Science, vol. 6, no. 11, 2024.
[8] J. Kaur, Suryakant, A. Pandey, and Arshika, “Sports analytics using computer vision: Performance analytics using computer vision and analysis techniques,” International Journal of Novel Research and Development (IJNRD), vol. 8, no. 1, 2023.
[9] Q. Zhang, L. Yu, and W. Yan, “AI-driven image recognition system for automated offside and foul detection in football matches using computer vision,” International Journal of Advanced Computer Science and Applications, vol. 16, no. 1, 2025.
[10] S. Gaikwad, H. Bhatt, D. Ghosh, and A. Malhan, “Visionary tracker: AI-powered sports analysis,” Computer Research and Development, vol. 25, no. 3, pp. 124–129, 2025.
[11] R. Bhatia, H. Bhatte, T. Bhoirekar, S. Lonkar, and B. Patil, “Football match analysis,” International Journal of Scientific Research in Engineering and Development, vol. 8, no. 2, pp. 2061–2068, Mar.–Apr. 2025.
[12] A. H. Mostafa, M. A. Rushdi, T. A. Basha, and K. Sayed, “FOOTBALLTrace: An AI-based system for football player tracking with occlusion detection and trajectory correction,” in Proc. 11th Int. Conf. Sport Sciences Research and Technology Support, 2023, pp. 1–8.
[13] O. Sorokivskyi, V. Hotovych, O. Nazarevych, and G. Shymchuk, “Comparative analysis of camera calibration algorithms for football applications,” Journal of Computer Vision in Sports, 2025.
[14] N. Darapaneni et al., “Detecting key soccer match events to create highlights using computer vision,” Journal of Advanced Computer Vision and Sports Analytics, 2025.
[15] S. Kagne, S. Sonawane, S. Shirke, and P. Chaudhari, “Computer vision-based sports performance analytics system,” International Journal of Progressive Research in Engineering, Management and Science, vol. 3, no. 5, pp. 1142–1148, May 2023.
