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General365
General365 is a manually curated benchmark for evaluating general reasoning in LLMs across difficult and diverse tasks, with a focus on reasoning over K-12-scope knowledge rather than domain-specialist knowledge.
The official repository presents General365 as the benchmark release for the paper on general reasoning under high difficulty and diversity, with public questions, variants, model-response formatting, grading code, project links, leaderboard materials, and a Hugging Face dataset link. This page is a starting point, not a recommendation. Check the original source before relying on the resource.
What it is
A general-reasoning benchmark
General365 is framed as a benchmark for testing broad reasoning ability, with manually crafted seed problems and variants intended to reduce overreliance on narrow domain knowledge or rote memorization.
Why it stands out
Difficulty and diversity focus
The notable angle is the combination of high-difficulty tasks, broad scenario coverage, K-12-scope knowledge constraints, held-out questions, and hybrid scoring that mixes rule-based and model-based checks.
Availability
Repo, dataset, project page, and leaderboard
The official materials include the GitHub repository, paper link, project page, leaderboard, Hugging Face dataset, grading script, model-response format, and example workflow for running evaluations.
Why it matters
Why readers may notice it
General365 matters because reasoning benchmarks can blur into knowledge tests. This project is positioned around separating general reasoning skill from specialist academic knowledge, while keeping tasks difficult and varied.
What readers may want to know
Where it fits
This belongs in the benchmark and dataset layer rather than the model, app, or agent layer. It is most relevant to readers comparing model reasoning evaluations and benchmark design choices.
Reporting note
What appears notable
Based on the repository, what readers may want to notice is the manually curated question design, 365 seed problems, 1,095 variants, held-out test-set note, leaderboard materials, and hybrid scoring workflow.
Before using
What readers may want to review
Which public questions, variants, and held-out limitations are described in the official materials.
The grading script, model-response JSONL format, and scoring method before adapting the benchmark.
Whether the benchmark is being used to compare general reasoning, data contamination risk, or a narrower model capability claim.
Best fit
Who may find it relevant
Readers following LLM reasoning benchmarks and model-comparison methods.
Builders and researchers comparing difficult general-reasoning tasks across model families.
Less relevant for readers looking for a model checkpoint, finished app, or agent framework.
Editorial note
Why it is included here
General365 is included because its source materials show reasoning evaluation across varied and difficult tasks, making it useful for readers comparing benchmark design beyond simple factual recall.
Source links
Original materials
Reader note
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