A recent paper in PLOS One made some noise in my twittersphere over the Christmas days. It compares the productivity of writing scientific documents using Microsoft Word and using LaTeX, and concludes that Microsoft Word is so clearly superior that, in the interest of saving taxpayers' money, scientific publishers should abandon LaTeX to allow authors to become more productive.
Scientific production in the era of Large Language Models
Large Language Models (LLMs) are rapidly reshaping scientific research. We analyze these changes in multiple, large-scale datasets with 2.1M preprints, 28K peer review reports, and 246M online accesses to scientific documents. We find: 1) scientists adopting LLMs to draft manuscripts demonstrate a large increase in paper production, ranging from 23.7-89.3% depending on scientific field and author background, 2) LLM use has reversed the relationship between writing complexity and paper quality, leading to an influx of manuscripts that are linguistically complex but substantively underwhelming, and 3) LLM adopters access and cite more diverse prior work, including books and younger, less-cited documents. These findings highlight a stunning shift in scientific production that will likely require a change in how journals, funding agencies, and tenure committees evaluate scientific works.
The new way we’ll do science
Papers should become human-readable views over a graph of data, tools, results, and certificates.
A budding discipline around how science gets read and judged - Aris
Scholarly publishing spent thirty years arguing about access. It has barely begun arguing about what readers do with the paper once they have it. At Aris we call that second argument scholarly interface design. Mike Morrison's community calls it ScienceUX. Either way it is becoming a field, and here is why it is worth your attention.
Are Scientific Papers Bad?
Is the scientific paper still a fraud?
How we write scientific papers does not reflect how we do science. Their formal structure infers a pre-ordained linear process rather than reflecting the messy creativity of research. This matters in the AI age because it masks the human in the process.
The unintended consequences of large language models as a labor-augmenting technology in science
As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.
The unintended consequences of large language models as a labor-augmenting technology in science
As a labor-augmenting technology, large language models (LLMs) have the potential to accelerate scientific activity across the research pipeline. But even if LLMs perform on par with human experts at selected tasks, their use will bring unintended consequences as they alter the balance of frictions and inducements that steer the allocation of research effort across projects. Here we develop a simple mathematical model to illustrate. In fields where LLMs are useful primarily as tools for discovering promising projects, researchers will become more selective about what they publish; where they facilitate the process of publishing existing data, researchers will become less selective. By allowing scientists to work more quickly, LLMs raise the opportunity cost of researcher time, creating incentives to refine papers less thoroughly before moving on. Enticing as it is to imagine that, by saving us time on mundane tasks, LLMs will provide us with more time to think deeply and develop projects completely, our results temper such hopes.
Paperstars
A better way to evaluate scientific papers. Methodological soundness and transparency, not citation counts.
Maybe scientific progress isn’t slowing, after all
A new paper takes aim at the claim that science has become less disruptive
A New Paradigm for Scientific Publishing, Peer Review, and Impact Assessment
Scientific publishing and peer review have evolved little in three centuries, while the demands placed on them have grown profoundly. The growing role of artificial intelligence has underscored deep, systemic shortcomings of an aging system that has largely evaded innovation, a system whose origins are appallingly closer to the invention of the printing press than to the internet. We can do better – much better. This article is intended as the beginning of a communal experiment: a living document that critically reviews the modern academic publishing and peer-review system and presents a concrete framework to address what bibliometrics experts¹ have characterized as "the pervasive misapplication of indicators to the evaluation of scientific performance". Building on the Leiden Manifesto, DORA, and a body of scholarship spanning many disciplines and decades, we present a community-governed, non-profit platform organized around three trust-weighted impact factors, for articles, authors, and reviewers, with full algorithmic transparency, an open development log, and structural decoupling of credibility scoring from content moderation and from monetization. We invite the community to discuss, critique, and help shape it.
Science should be machine-readable - Marginal REVOLUTION
One of the leading tasks of our time: We develop a machine-automated approach for extracting results from papers, which we assess via a comprehensive review of the entire eLife corpus. Our method facilitates a direct comparison of machine and peer review, and sheds light on key challenges that must be overcome in order to facilitate […]
Publish and Perish: How AI-Accelerated Writing Without...
Artificial intelligence tools are accelerating manuscript production far faster than peer review capacity can expand. Applying the theory of constraints from manufacturing science, we formalize...
Publish and Perish: How AI-Accelerated Writing Without...
Artificial intelligence tools are accelerating manuscript production far faster than peer review capacity can expand. Applying the theory of constraints from manufacturing science, we formalize...
The Bazaar of Scientific Knowledge | shishyko!
Is the current form of the scientific paper still optimal in 2025? How do we preserve, and efficiently leverage, the uncut gems of the scientific process?
Reclaiming scientific publishing: Our duty to make science freely accessible to all
When we (Camille Thomas and Romain Vaucher speaking) entered academia as graduate students in France and Switzerland, we were enthusiastic about the vast amount of research available with a simple click on our university computers. However, we also quickly felt disheartened by the significant amount of research work we couldn’t access when wrapping up our theses from home. Luckily, pirates existed. Empowered by Aaron Swartz’s Guerilla Open Access Manifesto, Alexandra Elbakyan created Sci-Hub in 2011, the greatest leak of scientific knowledge of the century. We felt right in the middle of an Open Access (OA) revolution that would finally make all scientific articles, old and new, accessible to everyone. Fifteen years later, our hopes as idealistic early-career researchers have been crushed by the oligopolistic model of scientific publishing and the subtle pressures of the “publish or perish” culture that reigns over our career development. In the meantime, publishers like Frontiers, MDPI and Springer Nature, to name a few, have exponentially expanded their number of titles. They have become increasingly exploitative of scholarly manpower, moving far away from the genuine accessibility they allegedly promised under the guise of this OA transformation. Open Access is to be praised, but the way it has been implemented through the mainstream Gold and Green OA models now primarily serves the status quo of large for-profit publishers. These entities hijack public money and voluntary editorial labour for their own profit and that of their shareholders (Butler et al., 2023; Shu and Larivière, 2024). In a nutshell, to offer reader accessibility, the Gold OA model requires authors to pay an Article Processing Charge (APC; around 2,000 $/€, rarely less, and often much more) covered by individual research funds, funding agencies or university library deals. The Green OA model allows authors to upload their accepted, non-formatted manuscripts to repositories after an embargo period. While both models allow compliance with funding agency mandates, true equity and accessibility are ultimately left behind. In an article we recently published (Vaucher and Thomas, 2026), we describe the mechanisms through which we, as researchers, inadvertently contribute to keeping research exclusive while driving the publishing model down an unsustainable path. Just like our broader economy, our publishing model and the ways science is evaluated fuelled a predatory system that demands more papers, funding and prestige at an ever-faster rate (Walter and Mullins, 2019), likely at the expense of quality, diversity and ethics (Frank et al., 2023; Heen and Vogt, 2024). These concerns aren’t entirely new. What is new, however, is the growing realisation among societies, universities and funding bodies that we must move away from this system. Initiatives like the European Diamond Capacity Hub, ALMASI and craft-OA are actively paving a way forward that we, as scientists, have yet to fully embrace. In the geosciences, a collective and concerted effort is currently being made by researchers to provide fairer, more sustainable alternatives through community-driven Diamond OA journals (which feature no APCs and completely free access to published articles). Volcanica (Farquharson and Wadsworth, 2018), Sedimentologika (Thomas et al., 2023), Tektonika (Fernández-Blanco et al., 2023), Seismica (Rowe et al., 2022), Geomorphica (Lefebvre et al., 2025), Open Paleontology (Drage et al., 2024), Advances in Geochemistry and Cosmochemistry (Pourret et al., 2025), Geodynamica, jSEDI and Planetary Research are all recently created, scholarly-run journals funded by university library investments to promote better ways of publishing. Their articles are peer-reviewed, free for readers to access, and free for authors to publish. They rely entirely on the voluntary involvement of scientists running open-source editorial platforms (such as Open Journal Systems), transparent workflows, copyediting, production and final dissemination. Diamond OA journals offer an alternative path for all of us to transform our broken publishing system and reclaim ownership of our own science. These efforts go hand in hand with greater involvement in our academic societies and non-profit publishing initiatives. Ultimately, real transformation can only happen if all of us as researchers realise how inherently unfair and exclusive the current system is to labs and institutions that cannot afford steep Gold OA APCs or paywalled journal subscriptions. It also means we must collectively stop evaluating science based on journal prestige and the flawed metrics they own (Posada and Chen, 2018; Sabel and Larhammar, 2025; Simons, 2008). Only by breaking these habits can we truly make knowledge accessible to all. References Butler, L.-A., Matthias, L., Simard, M.-A., Mongeon, P., and Haustein, S.: The oligopoly’s shift to open access: How the big five academic publishers profit from article processing charges, Quantitative Science Studies, 4, 778–799, https://doi.org/10.1162/qss_a_00272, 2023. Drage, H. B., Keating, J. N., Nielsen, M. L., Saleh, F., and Hearing, T. W. W.: Open Palaeontology: a new model of diamond open access journal for palaeontology, Open Palaeontology, 1, 1–6, https://doi.org/10.26034/la.opal.2024.6223, 2024. Farquharson, J. I. and Wadsworth, F. B.: Introducing Volcanica: The first diamond open-access journal for volcanology, Volcanica, 1, I–IX, https://doi.org/10.30909/vol.01.01.i-ix, 2018. Fernández-Blanco, D., Lacassin, R., Gouiza, M., Perez-Diaz, L., Magee, C., McCarthy, D., Doré, T., Péron-Pinvidic, G., Kavanagh, J., Bond, C., and Schmitt, R.: Tektonika: The Community-Led Diamond Open-Access Journal for Tectonics and Structural Geology, τeκτoniκa, 1, I–XIII, https://doi.org/10.55575/tektonika2023.1.1.56, 2023. Frank, J., Foster, R., and Pagliari, C.: Open access publishing – noble intention, flawed reality, Social Science & Medicine, 317, 115592, https://doi.org/10.1016/j.socscimed.2022.115592, 2023. Heen, E. and Vogt, H.: Scientific rot: Unsustainable publishing practices threatens trust in medicine, Journal of Evaluation in Clinical Practice, 30, 941–944, https://doi.org/10.1111/jep.13989, 2024. Lefebvre, A., Bosch, R., Burrows, K., Giaime, M., Goodwin, G., Lai, L. S.-H., Stammler, M., and Fernández, R.: Geomorphica: The most accessible journal for the geomorphology community, Geomorphica, 1, https://doi.org/10.59236/geomorphica.v1i1.54, 2025. Posada, A. and Chen, G.: Inequality in Knowledge Production: The Integration of Academic Infrastructure by Big Publishers, in: ELPUB 2018, https://doi.org/10.4000/proceedings.elpub.2018.30, 2018. Pourret, O., Millet, M.-A., Marin-Carbonne, J., Mallik, A., Tierney, J. E., Darling, J. R., Kiseeva, E. S., Torres, M. A., Fonseca, R. O. C., Tartèse, R., Namur, O., Klöcking, M., Matthews, S. W., Dahrén, B., Ickert, R. B., and Board, the inaugural A. in G. and C. editorial: Equitable Access, Open Science, and the Future of Publishing in Geochemistry and Cosmochemistry, Advances in Geochemistry and Cosmochemistry, 1, https://doi.org/10.33063/agc.v1i1.770, 2025. Rowe, C., Agius, M., Convers, J., Funning, G., Galasso, C., Hicks, S., Huynh, T., Lange, J., Lecocq, T., Mark, H., Okuwaki, R., Ragon, T., Rychert, C., Teplitzky, S., and Van den Ende, M.: The launch of Seismica: a seismic shift in publishing, Seismica, 1, https://doi.org/10.26443/seismica.v1i1.255, 2022. Sabel, B. and Larhammar, D.: Reformation of science publishing: the Stockholm Declaration, R Soc Open Sci., 12, 251805, https://doi.org/10.1098/rsos.251805, 2025. Shu, F. and Larivière, V.: The oligopoly of open access publishing, Scientometrics, 129, 519–536, https://doi.org/10.1007/s11192-023-04876-2, 2024. Simons, K.: The Misused Impact Factor, Science, 322, 165–165, https://doi.org/10.1126/science.1165316, 2008. Thomas, C., Privat, A., Vaucher, R., Spychala, Y., Zuchuat, V., Marchegiano, M., Poyatos-Moré, M., Kane, I., and Chiarella, D.: Sedimentologika: a community-driven diamond open access journal in sedimentology, Sedimentologika, 2023. Vaucher, R. and Thomas, C.: Diamond is the new Green—Why Green Open Access is not a sustainable long-term model for scientific publishing, Sedimentologika, 4, https://doi.org/10.57035/journals/sdk.2026.e41.2397, 2026. Walter, P. and Mullins, D.: From symbiont to parasite: the evolution of for-profit science publishing, Molecular Biology of the Cell, https://doi.org/https://doi.org/10.1091/mbc.E19-03-0147, 2019.
Konrad Hinsen's blog
Thirty years after my first contact with computational (ir)reproducibility, I am happy to note that many things have improved. Reproducibility, computational and otherwise, is increasingly recognized as an important aspect of scientific quality control, and mostly considered worth striving for. However, I also note that more and more people, including reproducibility activists, have lost contact with the day-to-day reality in which reproducibility matters. Reproducibility is becoming an item on a checklist, and its precise incarnation the subject of political bickering aimed at making it easy to check off that item. So let's take a look at why computational reproducibility matters for researchers.