Reed Bender, Karina Kofman, Blaise Agüera y Arcas, Michael Levin

The question of “what is life?” has challenged scientists and philosophers for centuries, producing an array of definitions that reflect both the mystery of its emergence and the diversity of disciplinary perspectives brought to bear on the question. Despite significant progress in our understanding of biological systems, psychology, computation, and information theory, no single definition for life has yet achieved universal acceptance. This challenge becomes increasingly urgent as advances in synthetic biology, artificial intelligence, and astrobiology challenge our traditional conceptions of what it means to be alive. We undertook a methodological approach that leverages large language models (LLMs) to analyze a set of definitions of life provided by a curated set of cross-disciplinary experts. We used a novel pairwise correlation analysis to map the definitions into distinct feature vectors, followed by agglomerative clustering, intra-cluster semantic analysis, and t-SNE projection to reveal underlying conceptual archetypes. This methodology revealed a continuous landscape of the themes relating to the definition of life, suggesting that what has historically been approached as a binary taxonomic problem should be instead conceived as differentiated perspectives within a unified conceptual latent space. We offer a new methodological bridge between reductionist and holistic approaches to fundamental questions in science and philosophy, demonstrating how computational semantic analysis can reveal conceptual patterns across disciplinary boundaries, and opening similar pathways for addressing other contested definitional territories across the sciences.

Read the full article at: arxiv.org

Sai Krishna Katla, Chenyu Lin, and Juan Pérez-Mercader

PNAS 122 (22) e2412514122

Self-reproduction is one of the most fundamental features of natural life. This study introduces a biochemistry-free method for creating self-reproducing polymeric vesicles. In this process, nonamphiphilic molecules are mixed and illuminated with green light, initiating polymerization into amphiphiles that self-assemble into vesicles. These vesicles evolve through feedback between polymerization, degradation, and chemiosmotic gradients, resulting in self-reproduction. As vesicles grow, they polymerize their contents, leading to their partial release and their reproduction into new vesicles, exhibiting a loose form of heritable variation. This process mimics key aspects of living systems, offering a path for developing a broad class of abiotic, life-like systems.

Read the full article at: www.pnas.org

Ryan Hill, Yian Yin, Carolyn Stein, Xizhao Wang, Dashun Wang & Benjamin F. Jones
Nature (2025)

Scientists and inventors set the direction of their work amid evolving questions, opportunities and challenges, yet the understanding of pivots between research areas and their outcomes remains limited1,2,3,4,5. Theories of creative search highlight the potential benefits of exploration but also emphasize difficulties in moving beyond one’s expertise6,7,8,9,10,11,12,13,14. Here we introduce a measurement framework to quantify how far researchers move from their existing work, and apply it to millions of papers and patents. We find a pervasive ‘pivot penalty’, in which the impact of new research steeply declines the further a researcher moves from their previous work. The pivot penalty applies nearly universally across science and patenting, and has been growing in magnitude over the past five decades. Larger pivots further exhibit weak engagement with established mixtures of prior knowledge, lower publication success rates and less market impact. Unexpected shocks to the research landscape, which may push researchers away from existing areas or pull them into new ones, further demonstrate substantial pivot penalties, including in the context of the COVID-19 pandemic. The pivot penalty generalizes across fields, career stage, productivity, collaboration and funding contexts, highlighting both the breadth and depth of the adaptive challenge. Overall, the findings point to large and increasing challenges in effectively adapting to new opportunities and threats, with implications for individual researchers, research organizations, science policy and the capacity of science and society as a whole to confront emergent demands.

Read the full article at: www.nature.com