SKYLENAGE Technical Report: Mathematical Reasoning and
  Contest-Innovation Benchmarks for Multi-Level Math Evaluation
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.
68df2f6edf49fb0df1e03c26
Hu Wei
68df2f6edf49fb0df1e03c27
Ze Xu
68df2f6edf49fb0df1e03c28
Boyu Yang
68df2f6edf49fb0df1e03c29
Linlin Miao
68df2f6edf49fb0df1e03c2a
Weiqi Zhai
68df2f6edf49fb0df1e03c2b
Yihan Li
68df2f6edf49fb0df1e03c2c
Zixuan Li
68df2f6edf49fb0df1e03c2d
Zhijun Wang
68df2f6edf49fb0df1e03c2e
Boya Wang
68df2f6edf49fb0df1e03c2f
Jianwei Yu
68df2f6edf49fb0df1e03c30
Jialing Yuan
68df2f6edf49fb0df1e03c31
Xiaoyue Zhang
68df2f6edf49fb0df1e03c32
Cheng He
68df2f6edf49fb0df1e03c33
Minglei Chen
68df2f6edf49fb0df1e03c34
Zifan Zhang
68df2f6edf49fb0df1e03c35
Qianhui Li
68df2f6edf49fb0df1e03c36
Wei Wang
68df2f6edf49fb0df1e03c37
Xiang Xu
SKYLENAGE Technical Report: Mathematical Reasoning and
  Contest-Innovation Benchmarks for Multi-Level Math Evaluation
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.
SKYLENAGE benchmarks evaluate LLMs on math reasoning, revealing performance gaps and ceiling effects across different educational levels.
large language models LLMs SKYLENAGE-ReasoningMATH SKYLENAGE-MATH structure-aware diagnostic set numeric density symbolic complexity contest-style suite subject x model performance grade x model performance mathematical reasoning