General365: Benchmarking General Reasoning in Large Language Models Across Diverse and Challenging Tasks
Contemporary large language models (LLMs) have demonstrated remarkable reasoning capabilities, particularly in specialized domains like mathematics and physics. However, their ability to generalize these reasoning skills to more general and broader contexts--often termed general reasoning--remains under-explored. Unlike domain-specific reasoning, general reasoning relies less on expert knowledge but still presents formidable reasoning challenges, such as complex constraints, nested logical branches, and semantic interference. To address this gap, we introduce General365, a benchmark specifically designed to assess general reasoning in LLMs. By restricting background knowledge to a K-12 le...
| Authors | Junlin Liu, Shengnan An, Shuang Zhou et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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