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Research community dynamics behind popular AI benchmarks


The widespread use of experimental benchmarks in AI research has created competition and collaboration dynamics that are still poorly understood. Here we provide an innovative methodology to explore these dynamics and analyse the way different entrants in these challenges, from academia to tech giants, behave and react depending on their own or others’ achievements. We perform an analysis of 25 popular benchmarks in AI from Papers With Code, with around 2,000 result entries overall, connected with their underlying research papers. We identify links between researchers and institutions (that is, communities) beyond the standard co-authorship relations, and we explore a series of hypotheses about their behaviour as well as some aggregated results in terms of activity, performance jumps and efficiency. We characterize the dynamics of research communities at different levels of abstraction, including organization, affiliation, trajectories, results and activity. We find that hybrid, multi-institution and persevering communities are more likely to improve state-of-the-art performance, which becomes a watershed for many community members. Although the results cannot be extrapolated beyond our selection of popular machine learning benchmarks, the methodology can be extended to other areas of artificial intelligence or robotics, and combined with bibliometric studies.

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Fig. 1: Progress in accuracy over time for ImageNet.
Fig. 2: Progress in accuracy over time for SQuAD1.1.
Fig. 3: Most prolific institutions (at least ten entries) in terms of total SOTA jumps entries and activity.

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Data availability

The data regarding all the papers analysed, their authors, community memberships, results for the different benchmarks and SOTA jumps can be found in the data folder on GitHub41 (‘data’ folder).

Code availability

The code for reproducing results can be found on GitHub41.


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F.M.-P. acknowledges funding from the AI-Watch project by DG CONNECT and DG JRC of the European Commission. J.H.-O. and S.Ó.h. were funded by the Future of Life Institute, FLI, under grant RFP2-152. J.H.-O. was supported by the EU (FEDER) and Spanish MINECO under RTI2018-094403-B-C32, Generalitat Valenciana under PROMETEO/2019/098 and European Union’s Horizon 2020 grant no. 952215 (TAILOR).

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The four authors, F.M.-P., P.B., S.Ó.h. and J.H.-O., participated in the definition and refinement of the goals of this study and the hypotheses. F.M.-P., J.H.-O. and P.B. conceived the technical methodology. P.B. and F.M.-P. implemented the code that collects and processes the data, and creates the communities. F.M.-P. generated the plots. All authors discussed the results and contributed to the writing of the final manuscript.

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Correspondence to Fernando Martínez-Plumed, Pablo Barredo, Seán Ó hÉigeartaigh or José Hernández-Orallo.

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The authors declare no competing interests.

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Peer review informationNature Machine Intelligence thanks Nima Dehmamy, Lars Kotthoff and Dashun Wang for their contribution to the peer review of this work.

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Supplementary Information

Supplementary analysis, Figs. 1–26 and Table 1.

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Martínez-Plumed, F., Barredo, P., hÉigeartaigh, S.Ó. et al. Research community dynamics behind popular AI benchmarks. Nat Mach Intell 3, 581–589 (2021).

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