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 level, General365 explicitly decouples reasoning from specialized expertise. The benchmark comprises 365 seed problems and 1,095 variant problems across eight categories, ensuring both high difficulty and diversity. Evaluations across 26 leading LLMs reveal that even the top-performing model achieves only 62.8% accuracy, in stark contrast to the near-perfect performances of LLMs in math and physics benchmarks. These results suggest that the reasoning abilities of current LLMs are heavily domain-dependent, leaving significant room for improvement in broader applications. We envision General365 as a catalyst for advancing LLM reasoning beyond domain-specific tasks toward robust, general-purpose real-world scenarios. Code, Dataset, and Leaderboard: https://general365.github.io
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Junlin Liu
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Shengnan An
claimed_verified
2026-04-14T08:15:33.150Z
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/avatars/69375ec915927b855813df8a6d486837.svg
Shengnan An
ShengnanAn
user
ShengnanAn
69ddaadcaea31e227c22c4a5
Shuang Zhou
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Dan Ma
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Shixiong Luo
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Ying Xie
69ddaadcaea31e227c22c4a9
Yuan Zhang
69ddaadcaea31e227c22c4aa
Wenling Yuan
69ddaadcaea31e227c22c4ab
Yifan Zhou
69ddaadcaea31e227c22c4ac
Xiaoyu Li
69ddaadcaea31e227c22c4ad
Ziwen Wang
69ddaadcaea31e227c22c4ae
Xuezhi Cao
69ddaadcaea31e227c22c4af
Xunliang Cai
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 level, General365 explicitly decouples reasoning from specialized expertise. The benchmark comprises 365 seed problems and 1,095 variant problems across eight categories, ensuring both high difficulty and diversity. Evaluations across 26 leading LLMs reveal that even the top-performing model achieves only 62.8% accuracy, in stark contrast to the near-perfect performances of LLMs in math and physics benchmarks. These results suggest that the reasoning abilities of current LLMs are heavily domain-dependent, leaving significant room for improvement in broader applications. We envision General365 as a catalyst for advancing LLM reasoning beyond domain-specific tasks toward robust, general-purpose real-world scenarios. Code, Dataset, and Leaderboard: https://general365.github.io
Large language models demonstrate limited general reasoning capabilities despite strong domain-specific performance, as revealed by a new benchmark assessing K-12 level reasoning across diverse problem types.
large language models general reasoning domain-specific reasoning benchmark reasoning capabilities semantic interference logical branches