SageMath Augmentation Boosts LLM Performance in Mathematics

While AI for mathematics has largely focused on autoformalization and theorem proving, the role of Computer Algebra Systems (CAS) in agentic LLM workflows remains underexplored. Researchers propose a ReAct-style agentic setup that combines LLM reasoning with verifiable feedback from SageMath, complemented by Context7 for up-to-date documentation. This system was evaluated on research-level mathematical problems from the RealMath benchmark, simulating a computational-mathematics research loop.The RealMath benchmark itself was refined with a multi-step post-processing and multi-stage validation pipeline to improve problem set quality. Experiments revealed substantial performance gains from SageMath access across all evaluated models, averaging a 9.7 percentage point increase, with gains ranging from 1.5 to 27.8 percentage points. This augmentation effectively narrowed the performance gap between open-weight and closed models.Qwen 3.7-Max benefited most from SageMath, while GPT-5.5 achieved the highest solve rate of 75.2% and the lowest token usage among tool-enabled configurations. These findings suggest that CAS-augmented agents are a promising direction for assisting mathematicians in computational exploration, potentially leading to automated conjecture discovery.