K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs
- 类型：arxiv
- 标识：2605.09635
- 链接：https://arxiv.org/abs/2605.09635
- 主分类：evaluation
- 形态：benchmark
- 被引：0
- 被引来源：Semantic Scholar + OpenAlex
- S2被引：0
- OpenAlex被引：0
- 影响力被引：0
- TLDR：This work introduces K12-KGraph, a curriculum-aligned knowledge graph extracted from official People's Education Press textbooks in mathematics, physics, chemistry, and biology across primary, middle, and high school, and derives K12-Bench, a 23,640-question multi-select benchmark with five task families, showing that textual and visual supervision are complementary.
- OpenAlex ID：W7160956167
- OpenAlex DOI：10.48550/arxiv.2605.09635
- DOI：10.48550/arxiv.2605.09635
- DOI来源：OpenAlex
- 开放获取：green
- 开放获取链接：https://doi.org/10.48550/arxiv.2605.09635
- OpenAlex更新：2026-08-24
- 副分类：engineering
- 待LLM分类：否
- 标题中文：K12-KGraph：用于教育 LLM 基准测试与训练的课程对齐知识图谱
- TLDR中文：本文提出 K12-KGraph，一个从人民教育出版社官方教材中提取的、与课程对齐的知识图谱，覆盖小学、初中和高中阶段的数学、物理、化学和生物，并由此衍生出 K12-Bench，一个包含 23,640 道题的多选基准，涵盖五类任务族，表明文本监督与视觉监督具有互补性。
- 来源文件：
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- /inbox/tom/_candidates/2026-07-25-agent-rag-longcontext-candidates.json
- /inbox/tom/_candidates/2026-07-26-agent-rag-longcontext-candidates.json
- /inbox/tom/_candidates/2026-07-27-agent-rag-longcontext-candidates.json
- /inbox/tom/_candidates/2026-07-27-agent-memory-tool-use-candidates.json
- [S2 enrich]
- [OpenAlex backfill]