SKYLENAGE Technical Report: Mathematical Reasoning and
  Contest-Innovation Benchmarks for Multi-Level Math Evaluation
Authors: name: Hu Wei; name: Ze Xu; name: Boyu Yang; name: Linlin Miao; name: Weiqi Zhai; name: Yihan Li; name: Zixuan Li; name: Zhijun Wang; name: Boya Wang; name: Jianwei Yu; name: Jialing Yuan; name: Xiaoyue Zhang; name: Cheng He; name: Minglei Chen; name: Zifan Zhang; name: Qianhui Li; name: Wei Wang; name: Xiang Xu
Published: 2025-09-24 02:09:32

Abstract: Large language models (LLMs) now perform strongly on many public math suites,
yet frontier separation within mathematics increasingly suffers from ceiling
effects. We present two complementary benchmarks: SKYLENAGE-ReasoningMATH, a
100-item, structure-aware diagnostic set with per-item metadata on length,
numeric density, and symbolic complexity; and SKYLENAGE-MATH, a 150-item
contest-style suite spanning four stages from high school to doctoral under a
seven-subject taxonomy. We evaluate fifteen contemporary LLM variants under a
single setup and analyze subject x model and grade x model performance. On the
contest suite, the strongest model reaches 44% while the runner-up reaches 37%;
accuracy declines from high school to doctoral, and top systems exhibit a
doctoral-to-high-school retention near 79%. On the reasoning set, the best
model attains 81% overall, and hardest-slice results reveal clear robustness
gaps between leaders and the mid-tier. In summary, we release
SKYLENAGE-ReasoningMATH and report aggregate results for SKYLENAGE-MATH;
together, SKYLENAGE provides a hard, reasoning-centered and broadly covering
math benchmark with calibrated difficulty and rich metadata, serving as a
reference benchmark for future evaluations of mathematical reasoning.