cs.AIOct 9, 2026

SqueezeBench: Saturation-Resilient Evaluation of Mathematical Capability

Authors: Pablo Manrique, Hai Xia, Stefan Szeider

Organizations: Algorithms and Complexity Group TU Wien, Vienna, Austria

Abstract

AI systems are making rapid progress in mathematics, but this progress is hard to measure. Benchmarks of solved problems saturate, and in benchmarks of open problems each problem can be resolved for the first time only once. We present SqueezeBench, a benchmark that is resilient to saturation: a success raises the bar instead of removing the problem. Each problem fixes an integer function f(n)f(n) whose values are known only for small nn, and the benchmark keeps a lower and an upper record on it. A record is a computable Lean function with a machine-checked proof that it bounds ff. A submission proposes a new function and proves that it bounds ff, that it is no worse than the current record at any nn, and that it is strictly better at some nn. Once accepted, it becomes the record that the next submission has to beat. The system thus chooses the bound as well as its proof, and it may improve the record at a single nn or its growth rate. At every nn, the gap between the two records shows how far the certified bounds are from determining f(n)f(n). Every proof is checked by two independent Lean kernels, so no referee needs to check the mathematics. The checks also reject a function that computes ff by exhaustive search at small nn, and a maintainer only reads the submitter's report and looks for such a search at larger nn. We release ten problems from extremal combinatorics with twenty proved initial records and the admission tools. As a baseline, agents based on a frontier language model, with at most one hour per record, had 82 submissions accepted and improved every record, without going beyond the published literature. Agents based on two newer models then started from these records, had 60 more submissions accepted and improved 19 of the 20 records again. For eight of the ten problems, at least one record is still asymptotically behind the best published bound.

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