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

Michael Levin and Richard Watson

Cell and developmental biology offer numerous remarkable examples of collective intelligence and adaptive plasticity to novel circumstances, as cells implement large-scale form and function. Many of these capabilities seem different from the behavior of machines or the results of computations. And yet, they are implemented by biochemical, biophysical, and bioelectrical events which are often interpreted with the machine metaphor that dominates molecular and cell biology. The seeming incongruity between molecular mechanisms and the emergence of self-constructing and goal-driven intentional living agents has driven a perennial debate between mechanist and organicist thinkers. Here, we discuss the inadequacies of, on the one hand, the (unminded) mechanist and computationalist frameworks, and on the other, dualistic conceptions of machine vs. mind. Both fail to provide an integration of agential and mechanistic aspects evident in biology. We propose that a new kind of cognitivism, cognition all the way down, provides the necessary unification of ‘bottom-up’ and ‘top-down’ causal flows evident in living systems. We illustrate how the organizational layers between genotype and phenotype provide problem-solving intelligence, not merely complexity, and discuss the benefits and inadequacies of specific machine metaphors in this context. By taking a pragmatist approach to the hypothesis that life and mind are fundamentally the same problem, formalisms are emerging that embrace the unique quality of the agential material of life while fully benefitting from the advances of modern machine science. New ways to map formal concepts of machine and data to biology provide a route toward unifying evolutionary and developmental biology, and rich substrates for the use of truly bio-inspired principles to advance engineering and computer science.

Read the full article at: osf.io

Ahmed Allibhoy, Arthur N. Montanari, Fabio Pasqualetti, Adilson E. Motter

Oscillator Ising machines (OIMs) are networks of coupled oscillators that seek the minimum energy state of an Ising model. Since many NP-hard problems are equivalent to the minimization of an Ising Hamiltonian, OIMs have emerged as a promising computing paradigm for solving complex optimization problems that are intractable on existing digital computers. However, their performance is sensitive to the choice of tunable parameters, and convergence guarantees are mostly lacking. Here, we show that lower energy states are more likely to be stable, and that convergence to the global minimizer is often improved by introducing random heterogeneities in the regularization parameters. Our analysis relates the stability properties of Ising configurations to the spectral properties of a signed graph Laplacian. By examining the spectra of random ensembles of these graphs, we show that the probability of an equilibrium being asymptotically stable depends inversely on the value of the Ising Hamiltonian, biasing the system toward low-energy states. Our numerical results confirm our findings and demonstrate that heterogeneously designed OIMs efficiently converge to globally optimal solutions with high probability.

Read the full article at: arxiv.org

Luís A. Nunes Amaral, Arthur Capozzi, Dirk Helbing

Organizations learn from the market, political, and societal responses to their actions. While in some cases both the actions and responses take place in an open manner, in many others, some aspects may be hidden from external observers. The Eurovision Song Contest offers an interesting example to study organizational level learning at two levels: organizers and participants. We find evidence for changes in the rules of the Contest in response to undesired outcomes such as runaway winners. We also find strong evidence of participant learning in the characteristics of competing songs over the 70-years of the Contest. English has been adopted as the lingua franca of the competing songs and pop has become the standard genre. Number of words of lyrics has also grown in response to this collective learning. Remarkably, we find evidence that four participating countries have chosen to ignore the "lesson" that English lyrics increase winning probability. This choice is consistent with utility functions that award greater value to featuring national language than to winning the Contest. Indeed, we find evidence that some countries -- but not Germany -- appear to be less susceptible to "peer" pressure. These observations appear to be valid beyond Eurovision.

Read the full article at: arxiv.org

David Deutsch, Chiara Marletto

Constructor theory asserts that the laws of physics are expressible as specifications of which transformations of physical systems can or cannot be brought about with unbounded accuracy by devices capable of operating in a cycle ('constructors'). Hence, in particular, such specifications cannot refer to time. Thus, laws expressed in constructor-theoretic form automatically avoid the anomalous properties of time in traditional formulations of fundamental theories. But that raises the problem of how they can nevertheless give meaning to duration and dynamics, and thereby be compatible with traditionally formulated laws. Here we show how.

Read the full article at: arxiv.org

Fernando Rosas, Alexander Boyd, Manuel Baltieri

Recent work proposes using world models to generate controlled virtual environments in which AI agents can be tested before deployment to ensure their reliability and safety. However, accurate world models often have high computational demands that can severely restrict the scope and depth of such assessments. Inspired by the classic `brain in a vat' thought experiment, here we investigate ways of simplifying world models that remain agnostic to the AI agent under evaluation. By following principles from computational mechanics, our approach reveals a fundamental trade-off in world model construction between efficiency and interpretability, demonstrating that no single world model can optimise all desirable characteristics. Building on this trade-off, we identify procedures to build world models that either minimise memory requirements, delineate the boundaries of what is learnable, or allow tracking causes of undesirable outcomes. In doing so, this work establishes fundamental limits in world modelling, leading to actionable guidelines that inform core design choices related to effective agent evaluation.

Read the full article at: arxiv.org

Linzhuo Li, Yiling Lin, Lingfei Wu

Using large-scale citation data and a breakthrough metric, the study systematically evaluates the inevitability of scientific breakthroughs. We find that scientific breakthroughs emerge as multiple discoveries rather than singular events. Through analysis of over 40 million journal articles, we identify multiple discoveries as papers that independently displace the same reference using the Disruption Index (D-index), suggesting functional equivalence. Our findings support Merton's core argument that scientific discoveries arise from historical context rather than individual genius. The results reveal a long-tail distribution pattern of multiple discoveries across various datasets, challenging Merton's Poisson model while reinforcing the structural inevitability of scientific progress.

Read the full article at: arxiv.org

Michael Levin

Advanced Intelligent Systems,

Recent discussions and debate around artificial intelligence (AI) and its status are notably incomplete, missing the implications of highly relevant aspects of the emerging fields of diverse intelligence (DI) and synthetic morphology, as well as of basic facts of developmental biology. Herein, it is argued that human flourishing is impossible without an appreciation of the space of possible beings and of the ways in which today's intelligent machine debates are about universal existential questions facing biological beings, not just AI. The inevitable arrival of a wide set of unconventional bodies and minds as humans modify and create new forms will disrupt untenable old narratives of what people are and how to recognize their sentient allies in unfamiliar guises. Herein, the issues engendered by the advent of AI from the perspective of the field of DI and the evolutionary history of the bodies and minds are discussed.

Read the full article at: advanced.onlinelibrary.wiley.com

Sudip Patra and Stuart Kauffman

We explore here the fundamental and striking paradigmatic shifts between ‘Domain of Laws’ and ‘Domain of No Laws’, where the former is an apt encapsulation of our remarkably successful but orthodox science world view (including classical physics and quantum mechanics) with well- defined and stable configuration spaces having deterministic or stochastic evolution, and the latter is a radically new Domain of No Law with evolving configuration spaces, non-deducible information creation, genuine novelties and an un-prestatable Adjacent Possible. We explore the features of these two distinct domains asking what can be defined with respect to work, energy, entropy, and agency. We offer a reconstruction of quantum mechanics to reframe traditional assumptions and address lingering questions concerning the nature of living, complex adaptive systems. We propose that a genuine responsible free will and a central role of agency are essential features of an evolving Biosphere. Here we extend this theme to call for a radically new and comprehensive view of science itself.

Read the full article at: osf.io

Hiroki Sayama

2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS)

Hash Chemistry, a minimalistic artificial chemistry model of open-ended evolution, has recently been extended to non-spatial and cellular versions. The non-spatial version successfully demonstrated continuous adaptation and unbounded growth of complexity (size) of self-replicating entities, but it did not simulate multiscale ecological interactions among the entities. On the contrary, the cellular version explicitly represented multiscale spatial ecological interactions among evolving patterns, yet it failed to show meaningful adaptive evolution or complexity growth. It remains an open question whether it is possible to create a similar minimalistic evolutionary system that can exhibit all of those desired properties at once, within a computationally efficient framework. Here we propose an improved version of Cellular Hash Chemistry, called “Structural Cellular Hash Chemistry” (SCHC). In SCHC, individual identities of evolving patterns are explicitly represented and processed as the connected components of the nearest neighbor graph of active (non-empty) cells. The neighborhood connections are established by connecting active cells with other active cells in their Moore neighborhoods in a 2D cellular grid. Evolutionary dynamics in SCHC are simulated via pairwise competitions of two randomly selected patterns, following the approach used in the non-spatial Hash Chemistry. SCHC's computational cost was significantly less than the original and non-spatial versions. Numerical simulations showed that these model modifications achieved spontaneous movement, self-replication and unbounded growth of complexity (size) of spatial evolving patterns, which were clearly visible in space in a highly intuitive manner. Detailed analysis of simulation results showed that there were spatial ecological interactions among self-replicating patterns and their diversity was also substantially promoted in SCHC, neither of which was present in the non-spatial version.

Read the full article at: ieeexplore.ieee.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

ARIEL FLINT ASHERY, LUCA MARIA AIELLO, AND ANDREA BARONCHELLI

SCIENCE ADVANCES 14 May 2025 Vol 11, Issue 20

Social conventions are the backbone of social coordination, shaping how individuals form a group. As growing populations of artificial intelligence (AI) agents communicate through natural language, a fundamental question is whether they can bootstrap the foundations of a society. Here, we present experimental results that demonstrate the spontaneous emergence of universally adopted social conventions in decentralized populations of large language model (LLM) agents. We then show how strong collective biases can emerge during this process, even when agents exhibit no bias individually. Last, we examine how committed minority groups of adversarial LLM agents can drive social change by imposing alternative social conventions on the larger population. Our results show that AI systems can autonomously develop social conventions without explicit programming and have implications for designing AI systems that align, and remain aligned, with human values and societal goals.

Read the full article at: www.science.org

Andy Clark 
Nature Communications volume 16, Article number: 4627 (2025)

As human-AI collaborations become the norm, we should remind ourselves that it is our basic nature to build hybrid thinking systems – ones that fluidly incorporate non-biological resources. Recognizing this invites us to change the way we think about both the threats and promises of the coming age.

Read the full article at: www.nature.com

Giulia Menichetti, Ph.D., Albert-László Barabási, Ph.D., and Joseph Loscalzo, M.D., Ph.D.

N Engl J Med 2025;392:1836-1845

Food contains more than 139,000 molecules, which influence nearly half the human proteome. Systematic analysis of food–chemical interactions can potentially advance nutrition science and drug discovery.

Read the full article at: www.nejm.org

Evangelos Pournaras, Srijoni Majumdar, Thomas Wellings, Joshua C. Yang, Fatemeh B. Heravan, Regula Hänggli Fricker, Dirk Helbing

Voting methods are instrumental design element of democracies. Citizens use them to express and aggregate their preferences to reach a collective decision. However, voting outcomes can be as sensitive to voting rules as they are to people's voting choices. Despite the significance and inter-disciplinary scientific progress on voting methods, several democracies keep relying on outdated voting methods that do not fit modern, pluralistic societies well, while lacking social innovation. Here, we demonstrate how one can upgrade real-world democracies, namely by using alternative preferential voting methods such as cumulative voting and the method of equal shares designed for a proportional representation of voters' preferences. By rigorously assessing a new participatory budgeting approach applied in the city of Aarau, Switzerland, we unravel the striking voting outcomes of fair voting methods: more winning projects with the same budget and broader geographic and preference representation of citizens by the elected projects, in particular for voters who used to be under-represented, while promoting novel project ideas. We provide profound causal evidence showing that citizens prefer proportional voting methods, which possess strong legitimacy without the need of very technical specialized explanations. We also reveal strong underlying democratic values exhibited by citizens who support fair voting methods such as altruism and compromise. These findings come with a global momentum to unleash a new and long-awaited participation blueprint of how to upgrade democracies.

Read the full article at: arxiv.org

Cogitate Consortium, Oscar Ferrante, Urszula Gorska-Klimowska, Simon Henin, Rony Hirschhorn, Aya Khalaf, Alex Lepauvre, Ling Liu, David Richter, Yamil Vidal, Niccolò Bonacchi, Tanya Brown, Praveen Sripad, Marcelo Armendariz, Katarina Bendtz, Tara Ghafari, Dorottya Hetenyi, Jay Jeschke, Csaba Kozma, David R. Mazumder, Stephanie Montenegro, Alia Seedat, Abdelrahman Sharafeldin, Shujun Yang, Sylvain Baillet, David J. Chalmers, Radoslaw M. Cichy, Francis Fallon, Theofanis I. Panagiotaropoulos, Hal Blumenfeld, Floris P. de Lange, Sasha Devore, Ole Jensen, Gabriel Kreiman, Huan Luo, Melanie Boly, Stanislas Dehaene, Christof Koch, Giulio Tononi, Michael Pitts, Liad Mudrik & Lucia Melloni

Nature (2025)

Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, but have not been directly compared3. Here we present an open science adversarial collaboration directly juxtaposing integrated information theory (IIT)4,5 and global neuronal workspace theory (GNWT)6,7,8,9,10 via a theory-neutral consortium11,12,13. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes and interpretation thereof12. Human participants (n = 256) viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography and intracranial electroencephalography. We found information about conscious content in visual, ventrotemporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results align with some predictions of IIT and GNWT, while substantially challenging key tenets of both theories. For IIT, a lack of sustained synchronization within the posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in the prefrontal cortex. These challenges extend to other theories of consciousness that share some of the predictions tested here14,15,16,17. Beyond challenging the theories, we present an alternative approach to advance cognitive neuroscience through principled, theory-driven, collaborative research and highlight the need for a quantitative framework for systematic theory testing and building.

Read the full article at: www.nature.com

ALEXANDER C. FURNAS, TIMOTHY M. LAPIRA, AND DASHUN WANG
SCIENCE 24 Apr 2025 Vol 388, Issue 6745 pp. 362-367 DOI: 10.1126/science.adt9895

Science has long been regarded as essential to policy-making, serving as one of the primary sources of evidence that informs decisions (1, 2) with its particular epistemic authority (3). Its role has become especially vital, as many pressing societal challenges today—from climate change to public health crises to technological advancement—are intricately linked with scientific progress. However, amid rising political polarization (4), a fundamental question remains open: Is science used differently by policy-makers in different parties? Here we combine two large-scale databases capturing policy, science, and their interactions to examine the partisan differences in citing science in policy-making in the United States. Overall, we observe systematic differences in the amount, content, and character of science cited in policy by partisan factions in the United States. These differences are strikingly persistent across fields of research, policy issues, time, and institutional contexts.

Read the full article at: www.science.org