Karina Kofman, et al.

Organisms. Journal of Biological Sciences

Life is one of the most fascinating features of the physical world. Despite centuries of scientific study, experts still disagree about the definition, and even the possibility or utility of a definition, of this field. In a recent paper, we used AI to analyze the conceptual space formed by definitions of life given by a select set of modern workers in the life sciences and related fields. However, some of the most interesting material emerged as real-time conversations among those polled. In order to ensure that these ideas are not lost to the peer-reviewed scientific record, we here provide a minimally-edited (largely verbatim) transcript of the email chain among leading thinkers, containing numerous clarifications, disagreements, and challenges that enrich the topic of Life. It is our hope that this case study serves as an example for future papers, since the exchange of ideas among scientists is at least as interesting and valuable as formal scientific manuscripts written from a single perspective.

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Andrea Roli, Sauro Succi, Stuart A. Kauffman

Front. Phys., 08 July 2026

Current AI technologies have demonstrated impressive results, mainly driven by large language models (LLMs). The most diffused applications of LLMs are in the so-called generative AI, which consists in techniques that produce texts, music, pictures or videos–often in a multimodal setting. Challenging the intuition that machines cannot be truly creative, the artefacts produced by LLMs are sometimes considered as surprising, novel and creative. This view is also supported by observing that there are both theoretical and practical limitations on the predictability of AI systems’ outcomes. Actual creativity can also be transformative and inventive, hence not just unpredictable but unprestatable: true novelty arises within a process whose evolution of the very possibility space cannot be predicted. Prominent examples of unprestatability are the evolution of the biosphere and can be found in artistic human productions. In this contribution, we elaborate on the notions of predictability and prestatability in the context of current AI systems. We maintain that these systems are, to some extent, unpredictable but not unprestatable. A consequence of our contention is the definition of the limits of what AI systems can and cannot do, and therefore the contexts for which these technologies are best suited.

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Jasper J van Beers, Marten Scheffer, Prashant Solanki, Ingrid A van de Leemput, Egbert H van Nes, Coen C de Visser

PNAS 123 (27) e2608847123

From aircraft to power grids, controlled systems form a crucial part of human societies. Nonetheless, catastrophic failures happen. Many of those arise from the accumulation of incremental problems, such as natural wear and tear, that can go unnoticed until it is too late. We demonstrate that generic indicators of resilience can detect growing instabilities in damaged drones. The generic nature of our approach makes it compatible across diverse controlled systems. This not only allows for on-the-fly warning of instability but also facilitates anomaly detection during manufacturing and promotes proactive maintenance. A complementary application is to use our indicators for exploratory design, allowing one to “tinker” with systems through small adjustments and sensing quickly whether those worsen or improve system resilience.

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Lorenzo Cirigliano, Gareth J. Baxter and Gábor Timár
Complexities 2026, 2(2), 13;

Real-world networks often exhibit strong transitivity with nontrivial local clustering spectra and degree correlations. Such features are not easily modeled in tractable network models, creating an obstacle to the theoretical understanding of such complex network structures. Here, we address this problem using a model for strongly clustered random graphs in which each triad of a random network backbone is closed with a certain probability. Despite the intricate loopy local structure of the graphs obtained, we provide exact expressions for the local clustering spectrum and the degree correlations, filling the gap in the theoretical description of this model for random graphs. In particular, we find positive degree assortativity accompanying high transitivity, and nontrivial structure in the clustering spectrum. Exact asymptotic analytical results, obtained for uncorrelated locally tree-like backbones, are complemented with extensive numerical characterization of finite-size effects.

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