评估用于计算和实验数学的 SageMath 增强 LLM 代理

arXiv:2607.06820v1 Announce Type: new Abstract: Recent advances in AI for Mathematics have focused largely on autoformalization and theorem proving, leaving the role of Computer Algebra Systems (CAS) in agentic LLM workflows underexplored.我们提出了一种 ReAct 风格的代理设置，它将 LLM 推理与来自 SageMath 的可验证反馈相结合，以及用于最新文档的 Context7。我们跨前沿模型评估这种代理设置，以在模拟计算数学研究循环的设置中解决 RealMath 基准的研究级数学问题。我们还建议通过引入多步骤后处理程序和多阶段验证管道来改进 RealMath 基准，这两者都提高了提取问题集的质量和可靠性。 Our experiments reveal substantial performance gains from SageMath access across all evaluated models on +9.7~pp on average, the gains range from 1.5~pp to 27.8~pp and narrow the gap between open-weight and closed models. Qwen~3.7-Max benefits from SageMath the most, while GPT-5.5 achieves the highest solve rate of $75.2\%$ and the lowest token usage among tool-enabled configurations. Our findings suggest that CAS-augmented agents represent a promising direction for assisting mathematicians in computational exploration, and we believe that this work is a step towards automated conjecture discovery.该项目存储库可在线获取。