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Abstract: As an experiment in agentic assisted analysis, we used an agentic AI model to analyse the effect of agentic AI on arXiv submissions; data acquisition, analysis and the first draft were carried out by the model under the author's direction. Monthly arXiv submissions have grown exponentially for three decades, at about $9\%$ per year. Fitting a piecewise log-linear trend with seasonal modulation to the full record, we find two post-pandemic breaks in growth rate. The first, in mid-2022, precedes ChatGPT and is carried by the expansion of AI research within computer science (CS); outside CS, growth from 2022 to 2025 remained at $8\%$ per year. The second, between December 2025 and February 2026, follows the release of models capable of extended autonomous work and is absent from the AI categories themselves. Since December 2025 submissions without a CS listing have run $17\%$ above trend, and in September 2026 total submissions reached a record $40\,363$. The excess is largest in mathematics, where submissions doubled within a year, and follows a gradient from formal and theoretical fields to experimental ones that is not ordered by surveyed rates of AI-tool adoption. In mathematics the increase is distributed across subfields and authors, two-thirds of it arising from a larger number of distinct authors rather than from a few prolific submitters. Across fields the clearest signature is a shift to single authorship: solo-authored papers rose by $70\%$ within a year in mathematics and condensed matter, and by $44\%$ in quantitative biology even as its total output fell, but not in experimental particle physics. The analysis is deliberately agnostic of LLM-detection methods: it uses only submission counts and metadata, with treatment timing fixed by calendar dates, and is therefore free of the outcome-triggered timing bias of author-level studies.

Abstract: Understanding who researchers collaborate with, when and how their research groups form, and how these evolve over time is a foundational prerequisite for constructing multidimensional, evidence-based researcher fingerprints -- a requirement that has become increasingly urgent in the context of ongoing reforms in research evaluation advocated by initiatives such as DORA, the Leiden Manifesto, and CoARA. In this paper, we propose an egocentric approach for fine-grained research group detection, operating at the scale of individual researchers' ego networks, as extracted from Scientific Knowledge Graphs (SKGs), and requiring the ego node to be present in all identified research groups -- a constraint that is not merely technical but reflects a commitment to researcher-centred analysis. Our approach builds on the Leiden algorithm as its computational core and is grounded in SKGs, which provide the heterogeneous, open scholarly metadata necessary to trace collaboration patterns at scale in a reproducible and transparent manner. We evaluate the proposed method through a user evaluation involving 10 researchers with diverse backgrounds and career stages, demonstrating its ability to surface meaningful and interpretable collaboration structures. The research groups identified constitute the structural scaffold upon which richer characterisations of scholarly identity -- including roles, seniority, contribution types, and publication patterns -- can be built in subsequent work, contributing to the broader goal of fairer and more transparent research assessment.