loading . . . “Papers with Code” went offline, the knowledge doesn’t have to Launched in 2018, Papers with Code was a community-driven platform for exploring and discovering state-of-the-art research in artificial intelligence and machine learning. Within one year, it became a crucial infrastructure for the computer science community, growing into a resource with more than 18,000 papers and over 1,500 leaderboards (1). The platform aggregated studies from multiple sources and served as a central hub for benchmarked research in the form of leaderboards (see an example of a leaderboard here). It also supported the open science movement by publishing academic papers with their source code.
In response to this rapid growth, Papers with Code announced it was joining Facebook AI in 2019. Users were reassured that “Papers with Code [would] remain a neutral, open and free resource” and there would be no changes to their services. Yet, earlier this year, the Papers with Code website suddenly went offline. Without prior notice, users were simply redirected to the Papers with Code Github repository, and the machine learning community was left to wonder about the fate of this key resource.
About one month after the platform disappeared, Hugging Face, a private company providing collaborative platforms for machine learning models, released a LinkedIn announcement that it was building a successor platform in partnership with Papers with Code and Meta (formerly Facebook). While the Hugging Face website allows the research community to follow trending papers linked to their source code, it has only recently integrated leaderboard functionality. Unlike Papers with Code, which curated paper-centric leaderboards for a wide range of tasks, Hugging Face leaderboards focus on model-centric, reproducible evaluation pipelines such as the Open LLM Leaderboard, enabling users to compare deployed models under standardized conditions. One reason Papers with Code scaled so well is that it allowed researchers to submit performance results from **any** model (as reported in their papers), regardless of where the model was hosted. In contrast, Hugging Face’s current leaderboard setup requires the model to be publicly hosted on the Hugging Face Hub, loadable via supported APIs. This excludes many works that report results but do not deploy models in that way, limiting visibility into progress across all research.
## The role of public institutions in safeguarding research data
The Open Research Knowledge Graph (ORKG) is an open-source and open-data project at the TIB-Leibniz Information Centre for Science and Technology that also enables benchmarked tracking of state-of-the-art research through comparisons (see Fig. 1). As a national library and foundation of public law under the German state of Lower Saxony, the TIB’s mission is to ensure sustainable access to information and digital data of high public value.
Figure 1: An ORKG comparison of crowdsourcing and annotation strategies for question answering tasks in natural language processing and vision, accessible on the ORKG platform.
In 2021, the ORKG imported data from Papers with Code, capturing benchmarks that would have been lost if the Papers with Code website had gone offline (see Fig. 2). This highlights the importance of redundancy across digital infrastructures. If benchmarks are available only on commercial platforms, they remain vulnerable to corporate decisions, shifting business models, or sudden shutdowns. Public infrastructures, such as the ORKG, ensure that this knowledge remains accessible over the long term. This is a crucial example of the role public institutions play in providing continuity, safeguarding scientific knowledge, and ensuring that resources developed by and for the community do not simply disappear.
Figure 2: An ORKG leaderboard for question answering models, accessible on the ORKG platform.
## A call to the community
Continuity also requires participation. The strength of public infrastructures, such as the ORKG, depends on the level of community engagement. Keeping leaderboards populated with the latest benchmarks requires researchers to contribute their results. Here is our call to action: if you were disappointed to see Papers with Code discontinued, consider contributing your papers to the ORKG. Your contributions ensure the leaderboards tracking progress in your field remain open and accessible to everyone.
### About the ORKG
The Open Research Knowledge Graph (ORKG) is a service that aims to revolutionise the way scientific knowledge is shared and used. By creating a structured, searchable knowledge graph, the ORKG makes scientific information more accessible and usable for the global research community.
### Reference
(1) https://medium.com/paperswithcode/papers-with-code-is-joining-facebook-ai-90b51055f694
Schlagwörter:
Papers with Code
Open Science
Open Research Knowledge Graph https://blog.tib.eu/2025/10/02/papers-with-code-went-offline-the-knowledge-doesnt-have-to/
