Potential limitations of the MACM approach include:
Computational Complexity: MACM may face challenges in scalability and computational efficiency when handling extremely complex mathematical problems. Future research could focus on optimizing the algorithms and data structures used in MACM to improve computational performance.
Limited Generalization: While MACM demonstrates strong generalizability across various mathematical contexts, there may still be limitations in handling entirely novel problem types. Addressing this limitation could involve incorporating transfer learning techniques to adapt MACM to new problem domains.
Dependency on Large Language Models: MACM heavily relies on large language models like GPT-4 for inference and decision-making. Future research could explore reducing this dependency by developing more specialized problem-solving algorithms within MACM.
Interpretability: The inner workings of MACM, especially the decision-making process of the Judge agent, may lack transparency. Future research could focus on enhancing the interpretability of MACM's reasoning and decision-making steps for better understanding and validation.