---
title: "open-compass/opencompass: papers citing it"
description: "4 papers in the tracked journals link to the GitHub repository open-compass/opencompass in their Crossref records, the first in 2024; most of them (2) in 2025."
url: https://scholar4ai.com/code/github-open-compass-opencompass
markdown_url: https://scholar4ai.com/code/github-open-compass-opencompass.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to the GitHub repository open-compass/opencompass in their Crossref records, the first in 2024; most of them (2) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# open-compass/opencompass: papers linking to it

Research papers in the 480 tracked journals that link to open-compass/opencompass (https://github.com/open-compass/opencompass), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- GitHub
- 7,563 stars
- 899 forks
- Python
- license Apache-2.0
- created 2023
- https://github.com/open-compass/opencompass

All repositories: https://scholar4ai.com/code.md

## Key findings

- 4 papers in the tracked journals link to the GitHub repository open-compass/opencompass in their Crossref records, the first in 2024; most of them (2) in 2025.
- By category: AI (4); the journals with the most are Neural Networks (1), Nature Machine Intelligence (1) and Expert Systems with Applications (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links open-compass/opencompass.

## By year

*Papers linking to open-compass/opencompass by publication year*

| Year | Papers |
|---|---:|
| 2024 | 1 |
| 2025 | 2 |
| 2026 | 1 |

## Journals

*Journals whose papers link to open-compass/opencompass most*

| Journal | Papers |
|---|---:|
| [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | 1 |
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 1 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Pay more attention to the robustness of LLMs on adversarial prompt for instruction data mining](https://doi.org/10.1016/j.neunet.2025.107989) (Neural Networks, 2025)
  - [Model compression-driven instruction data mining framework with integrated adversarial attack strategies](https://doi.org/10.1016/j.eswa.2026.132870) (Expert Systems with Applications, 2026) — cites the paper above

## Papers

*Papers linking to open-compass/opencompass, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2025-11-06 | [Densing law of LLMs](https://doi.org/10.1038/s42256-025-01137-0) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Chaojun Xiao, Jie Cai, Weilin Zhao et al. | 24 | cites it |
| 2025-08-22 | [Pay more attention to the robustness of LLMs on adversarial prompt for instruction data mining](https://doi.org/10.1016/j.neunet.2025.107989) | [Neural Networks](https://scholar4ai.com/journals/neural-networks.md) | Qiang Wang, Dawei Feng, Xu Zhang et al. | 1 | cites it |
| 2026-05-14 | [Model compression-driven instruction data mining framework with integrated adversarial attack strategies](https://doi.org/10.1016/j.eswa.2026.132870) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Qiang Wang, Ao Shen, Yifan Zeng et al. | 1 | cites it |
| 2024-09-06 | [Targeted training for numerical reasoning with large language models](https://doi.org/10.1007/s10115-024-02216-1) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Li Xiao, Sichen Liu, Yin Zhu et al. | 0 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). open-compass/opencompass: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-open-compass-opencompass

```bibtex
@misc{scholar4ai-code-github-open-compass-opencompass,
  author = {{Scholar4AI}},
  title = {open-compass/opencompass: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-open-compass-opencompass}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).