arXiv:2604. 07530v2 Announce Type: replace-cross Abstract: Scaling laws describe how language model capabilities grow with compute and data, but say nothing about how long a model matters once released.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
The study examined biomedical research articles from 2022 to March 2026 that employed large language models (LLMs). It found that 42% of the most frequently used models were already retired or scheduled to retire within two years of publication, with a median retirement interval of 538 days. This high rate of model deprecation threatens the reproducibility of biomedical AI research.
arXiv:2607. 24032v1 Announce Type: new Abstract: Generative-AI evaluations can become historical before publication, yet calendar age does not affect every conclusion equally.
Generative-AI evaluations can become historical before publication, yet calendar age does not affect every conclusion equally. This paper has two linked purposes.
The article examines how medical research lags behind the rapid evolution of large language models (LLMs). From January 2023 to June 2026, PubMed records in fourteen clinical domains grew 45‑fold, yet only 2.5 % employed randomized, controlled, or prospective designs. The evaluation gap widened from 1.33 to 6.08 quarters, with randomized trials assessing models that were on average 4.6 quarters older than other studies, and 62 % of such trials evaluated discontinued model families.
The paper introduces SciUtopia, a closed‑loop large‑language‑model simulation framework that models the evolving academic research ecosystem, including research direction, collaboration, publication, peer review, funding, and researcher attrition. Running 61 simulation worlds, the system simulates over 40,000 researchers and 1.2 million LLM‑generated peer reviews, revealing that rejection‑driven resubmission increases reviewer burden, cautious exploration balances citation impact with career success, and resource inequality can arise without early‑funding advantage.
arXiv:2607. 21327v1 Announce Type: cross Abstract: Bibliometric indicators - citation counts, h-indexes, co-authorship networks - have long anchored science, technology, and innovation (STI) analytics, yet suffer from temporal lag, semantic shallowness, and an inability to capture the non-linear dynamics of contemporary knowledge ecosystems.