====== Soohak Math Benchmark ======
The **Soohak Math Benchmark** is a research-level mathematical evaluation dataset designed to assess the mathematical reasoning capabilities of large language models and other AI systems on problems that extend beyond traditional olympiad-style mathematics. Introduced in 2026, the benchmark comprises 439 original problems authored collaboratively by 64 mathematicians, including 38 faculty members from various institutions, with the explicit goal of creating assessment challenges that remain difficult for frontier-class AI models (([[https://news.smol.ai/issues/26-05-12-not-much/|AI News - Soohak Math Benchmark (2026]])), (([[https://www.latent.space/p/ainews-the-end-of-finetuning|Latent Space - Soohak (2026]]))

===== Benchmark Design and Composition =====
The Soohak Math Benchmark distinguishes itself through its original authorship and deliberate scope design. Rather than relying on existing mathematical problems from olympiad competitions or standard educational curricula, the benchmark's 439 problems were created specifically to test mathematical reasoning beyond conventional competition mathematics. The involvement of 64 mathematicians in problem creation—including a significant proportion of academic faculty—ensures diverse mathematical expertise and problem domains across the dataset (([[https://news.smol.ai/issues/26-05-12-not-much/|AI News - Soohak Math Benchmark (2026]])), (([[https://www.latent.space/p/ainews-the-end-of-finetuning|Latent Space - Soohak (2026]]))

This collaborative authorship model addresses a critical limitation in existing mathematical benchmarks: the tendency for frontier models to approach saturation on standard olympiad problems through memorization or pattern matching during training. By commissioning original problems, the Soohak benchmark aims to maintain discriminative power in evaluating model capabilities, preventing the artificial ceiling effects observed when models achieve high performance on existing, widely-distributed datasets.

===== Purpose and Research Applications =====
The primary purpose of the Soohak Math Benchmark is to serve as a challenging evaluation tool for assessing the mathematical reasoning capabilities of large language models and frontier AI systems. As models continue to improve, benchmarks that remain challenging are essential for meaningful capability assessment and research progress measurement. The benchmark addresses the practical research need for evaluation datasets that do not saturate quickly as models advance, allowing researchers to distinguish between different levels of mathematical reasoning ability.

The benchmark is particularly valuable for researchers investigating deep mathematical reasoning, theorem proving, problem-solving strategies, and the transfer of mathematical knowledge across different problem domains. By including problems explicitly designed to be difficult for frontier models, the benchmark enables fine-grained analysis of model limitations and the specific types of mathematical reasoning where current systems struggle most severely (([[https://news.smol.ai/issues/26-05-12-not-much/|AI News - Soohak Math Benchmark (2026]])), (([[https://www.latent.space/p/ainews-the-end-of-finetuning|Latent Space - Soohak (2026]]))

===== Positioning Within AI Evaluation =====
The Soohak Math Benchmark represents an important contribution to the broader AI evaluation landscape, where standardized benchmarks play a critical role in measuring progress and identifying capability boundaries. Mathematical reasoning benchmarks occupy a particularly important position in AI research, as mathematics represents a domain where human reasoning is well-defined, objective evaluation is straightforward, and performance gaps between systems are clear and measurable.

The benchmark joins established mathematical evaluation datasets in addressing different aspects of mathematical capability assessment. While existing benchmarks may emphasize speed, breadth, or alignment with educational standards, the Soohak benchmark's design prioritizes challenge level and resistance to saturation, making it particularly suited for distinguishing capabilities at the frontier of current model performance.


===== See Also =====
  * [[matharena|MathArena]]
  * [[frontier_math|FrontierMath Benchmark]]
  * [[gpqa_diamond|GPQA-Diamond]]
  * [[tau2_bench|Tau2-Bench]]
  * [[model_benchmarking_and_evaluation|Model Benchmarking and Evaluation]]

===== References =====