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

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.