SABER-Math: Automated Benchmark for Information Retrieval Evaluation in Mathematics
As agentic AI systems tackle more complex mathematical tasks, they increasingly rely on information retrieval (IR) to search problem databases, theorem libraries, and educational resources. However, choosing the right retriever remains difficult, as it is infeasible to directly isolate its effect on downstream performance. On the other hand, existing retrieval-specific benchmarks often fail to capture fine-grained mathematical relevance, penalizing relevant documents. We address this gap by introducing SABER-Math, the first fully automated benchmark for evaluating mathematical IR without expert annotation. Starting from 283K high-school-level math problems with solutions, SABER-Math build...
| Authors | Nikolay Georgiev, Maria Drencheva, Kseniia Ibragimova et al. |
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| Primary category | cs.IR |
| Published | 29 June 2026 |
| Last updated at source | 2 September 2026 |
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