Knowledge Index of Noah's Ark

A High-Density Benchmark Systematically Mapping 261 Disciplines

ArXiv Code Dataset Leaderboard

Overview

KINA is a high-density knowledge benchmark encompassing 261 fine-grained disciplines, the first to incorporate disciplinary representativeness as a core metric. It features a reusable, game-theoretic data collection pipeline that mitigates annotation vulnerabilities.

261Disciplines
899Questions
10Options

Benchmark Comparison

Bubble size = question count  ·  Lower score = more challenging for SOTA models

KINA (Ours)
Other Benchmarks

Leaderboard

We evaluate 45 models from 13 major AI labs on KINA. Scores are reported as avg@4 accuracy.

Filter:
Rank Model Type ALL ▲ Agr. ▲ Econ. ▲ Edu. ▲ Eng. ▲ Hist. ▲ Law ▲ Arts ▲ Mgt. ▲ Med. ▲ Phil. ▲ Sci. ▲ Soc. ▲
Closed-Source
Open-Source
1 Gold
2 Silver
3 Bronze
Bold = Best in column

Data Sample

Data Collection Pipeline

Data Collection Pipeline

Score Distribution

Granularity:

Hover to see statistics. Click a violin to jump to the model in the leaderboard.

Discipline Coverage

We curate a hierarchical taxonomy of Disciplines grounded in the U.S. Classification of Instructional Programs (CIP).
The finalized dataset comprises 899 instances, distributed across 12 disciplines, 70 fields, and 261 fine-grained subfields.

All 12 Disciplines · 70 Fields · 261 Fine-grained Subfields · 899 Questions

Click any block to drill into its Level-3 sub-disciplines. Click the breadcrumb to return.

Model Scores Over Time

Hover a dot to see score and release date. Click to jump to the model in the leaderboard.

Inference Cost Distribution

Qwen3

Qwen3.5

Token Length vs. Performance

BibTeX

If you find KINA useful in your research, please cite our paper:

@misc{jin2026knowledgeindexnoahsark,
    title={Knowledge Index of Noah's Ark}, 
    author={Sheng Jin and Minghao Liu and Yunze Xiao and Zeqi Zhou and Heli Qi and Yifan 
            Yao and Meishu Song and Kaijing Ma and Xuan Zhang and Sicong Jiang and Yizhe
            Li and Ningshan Ma and Jie Wei and Ziniu Li and Minglai Yang and Bangya Liu
            and Yiming Liang and Xiao Fang and Qingcheng Zeng and Jiarui Liu and Rui Yang
            and Shen Yan and Wenhao Huang and Jiaheng Liu and Zihan Wang and Weihao Xuan 
            and Ge Zhang},
    year={2026},
    eprint={2606.05104},
    archivePrefix={arXiv},
    primaryClass={cs.AI},
    url={https://arxiv.org/abs/2606.05104}, 
}