With the recent Nobel Prize awarded for radical advances in protein discovery, foundation models (FMs) for exploring large combinatorial spaces promise to revolutionize many scientific fields. Artificial Life (ALife) has not yet integrated FMs, thus presenting a major opportunity for the field to alleviate the historical burden of relying chiefly on manual design and trial-and-error to discover the configurations of lifelike simulations. This paper presents, for the first time, a successful realization of this opportunity using vision-language FMs. The proposed approach, called Automated Search for Artificial Life (ASAL), (1) finds simulations that produce target phenomena, (2) discovers simulations that generate temporally open-ended novelty, and (3) illuminates an entire space of interestingly diverse simulations. Because of the generality of FMs, ASAL works effectively across a diverse range of ALife substrates including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. A major result highlighting the potential of this technique is the discovery of previously unseen Lenia and Boids lifeforms, as well as cellular automata that are open-ended like Conway's G
Foundation models (FMs) have recently opened up new frontiers in the field of artificial life (ALife) by providing powerful tools to automate search through ALife simulations. Previous work aligns ALife simulations with natural language target prompts using vision-language models (VLMs). We build on Automated Search for Artificial Life (ASAL) by introducing ASAL++, a method for open-ended-like search guided by multimodal FMs. We use a second FM to propose new evolutionary targets based on a simulation's visual history. This induces an evolutionary trajectory with increasingly complex targets. We explore two strategies: (1) evolving a simulation to match a single new prompt at each iteration (Evolved Supervised Targets: EST) and (2) evolving a simulation to match the entire sequence of generated prompts (Evolved Temporal Targets: ETT). We test our method empirically in the Lenia substrate using Gemma-3 to propose evolutionary targets, and show that EST promotes greater visual novelty, while ETT fosters more coherent and interpretable evolutionary sequences. Our results suggest that ASAL++ points towards new directions for FM-driven ALife discovery with open-ended characteristics.
Even when concepts similar to emergence have been used since antiquity, we lack an agreed definition. However, emergence has been identified as one of the main features of complex systems. Most would agree on the statement ``life is complex''. Thus, understanding emergence and complexity should benefit the study of living systems. It can be said that life emerges from the interactions of complex molecules. But how useful is this to understand living systems? Artificial life (ALife) has been developed in recent decades to study life using a synthetic approach: build it to understand it. ALife systems are not so complex, be them soft (simulations), hard (robots), or wet (protocells). Then, we can aim at first understanding emergence in ALife, for then using this knowledge in biology. I argue that to understand emergence and life, it becomes useful to use information as a framework. In a general sense, I define emergence as information that is not present at one scale but is present at another scale. This perspective avoids problems of studying emergence from a materialist framework, and can be also useful in the study of self-organization and complexity.
Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classificat
In living systems, we often see the emergence of the ingredients necessary for computation -- the capacity for information transmission, storage, and modification -- begging the question of how we may exploit or imitate such biological systems in unconventional computing applications. What can we gain from artificial life in the advancement of computing technology? Artificial life provides us with powerful tools for understanding the dynamic behavior of biological systems and capturing this behavior in manmade substrates. With this approach, we can move towards a new computing paradigm concerned with harnessing emergent computation in physical substrates not governed by the constraints of Moore's law and ultimately realize massively parallel and distributed computing technology. In this paper, we argue that the lens of artificial life offers valuable perspectives for the advancement of high-performance computing technology. We first present a brief foundational background on artificial life and some relevant tools that may be applicable to unconventional computing. Two specific substrates are then discussed in detail: biological neurons and ensembles of nanomagnets. These substrate
Deep AndersoNN accelerates AI by exploiting the continuum limit as the number of explicit layers in a neural network approaches infinity and can be taken as a single implicit layer, known as a deep equilibrium model. Solving for deep equilibrium model parameters reduces to a nonlinear fixed point iteration problem, enabling the use of vector-to-vector iterative solvers and windowing techniques, such as Anderson extrapolation, for accelerating convergence to the fixed point deep equilibrium. Here we show that Deep AndersoNN achieves up to an order of magnitude of speed-up in training and inference. The method is demonstrated on density functional theory results for industrial applications by constructing artificial life and materials `scientists' capable of classifying drugs as strongly or weakly polar, metal-organic frameworks by pore size, and crystalline materials as metals, semiconductors, and insulators, using graph images of node-neighbor representations transformed from atom-bond networks. Results exhibit accuracy up to 98\% and showcase synergy between Deep AndersoNN and machine learning capabilities of modern computing architectures, such as GPUs, for accelerated computatio
This text introduces the twin deadlocks of strong artificial life. Conceptualization of life is a deadlock both because of the existence of a continuum between the inert and the living, and because we only know one instance of life. Computationalism is a second deadlock since it remains a matter of faith. Nevertheless, artificial life realizations quickly progress and recent constructions embed an always growing set of the intuitive properties of life. This growing gap between theory and realizations should sooner or later crystallize in some kind of paradigm shift and then give clues to break the twin deadlocks.
Complex systems fail. I argue that failures can be a blueprint characterizing living organisms and biological intelligence, a control mechanism to increase complexity in evolutionary simulations, and an alternative to classical fitness optimization. Imitating biological successes in Artificial Life and Artificial Intelligence can be misleading; imitating failures offers a path towards understanding and emulating life it in artificial systems.
This philosophical paper explores the relation between modern scientific simulations and the future of the universe. We argue that a simulation of an entire universe will result from future scientific activity. This requires us to tackle the challenge of simulating open-ended evolution at all levels in a single simulation. The simulation should encompass not only biological evolution, but also physical evolution (a level below) and cultural evolution (a level above). The simulation would allow us to probe what would happen if we would "replay the tape of the universe" with the same or different laws and initial conditions. We also distinguish between real-world and artificial-world modelling. Assuming that intelligent life could indeed simulate an entire universe, this leads to two tentative hypotheses. Some authors have argued that we may already be in a simulation run by an intelligent entity. Or, if such a simulation could be made real, this would lead to the production of a new universe. This last direction is argued with a careful speculative philosophical approach, emphasizing the imperative to find a solution to the heat death problem in cosmology. The reader is invited to c
Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural and computational sciences. Artificial life aims to foster a comprehensive study of life beyond "life as we know it" and towards "life as it could be", with theoretical, synthetic and empirical models of the fundamental properties of living systems. While still a relatively young field, artificial life has flourished as an environment for researchers with different backgrounds, welcoming ideas and contributions from a wide range of subjects. Hybrid Life is an attempt to bring attention to some of the most recent developments within the artificial life community, rooted in more traditional artificial life studies but looking at new challenges emerging from interactions with other fields. In particular, Hybrid Life focuses on three complementary themes: 1) theories of systems and agents, 2) hybrid augmentation, with augmented architectures combining living and artificial systems, and 3) hybrid interactions among artificial and biological systems. After discussing some of the major sources of inspiration for these themes, we will
Amino acids (AAs) are a key target in the search for life beyond Earth due to their extensive role in the machinery of all known life, persistence over geologic timescales, and analytical detectability. However, AAs can also arise from abiotic processes on planets and in space. For example, material from asteroid Bennu contained 33 AAs, including 15 of the 20 proteinogenic AAs that are fundamental to life's functions. Distinguishing life from non-life based on AAs in a sample remains an unsolved problem, particularly when their isotopic and structural signatures (e.g., chirality) could be altered via physicochemical processes. Here we introduce LUMOS (Life Unveiled via Molecular Orbital Signatures), a statistical framework that distinguishes life from non-life by analyzing the distribution of abundance-weighted HOMO-LUMO gap (HLG) values of AAs within a sample. Compilation of AAs datasets from diverse environments and provenances revealed that abiotic samples display highly uniform distributions of AAs HLGs. In contrast, biotic samples show greater variance and preference towards AAs with lower HLG, likely reflecting the need for life to control when, where, and how chemical reacti
We investigate how well the Large Interferometer for Exoplanets (LIFE) mission concept can detect habitable conditions on exoplanets through the presence of atmospheric water vapor as a proxy for surface oceans. We model the atmosphere of a pre-biotic Earth-like planet across a range of water concentrations, from water-poor to water-rich, with surface partial pressures from 10$^{-7}$ to 1 bar of H$_2$O. We simulate LIFE-like noise at spectral resolutions R = 50 and 100 using LIFEsim and perform Bayesian atmospheric retrievals to determine the technical requirements for LIFE to confirm habitability. We model three vertical water distributions: a vertically constant profile, a Manabe-Wetherald based Earth-like profile, and a diffusion and photochemistry profile to test how the assumed vertical structure influences the retrieved abundances. Clouds are not modeled. We find the ability for LIFE to detect water strongly depends on the vertical profile assumed. LIFE is unable to constrain the highest water cases and provides upper limits on low water planets. For the highest water abundances, absorption features saturate and reduce sensitivity to characterize precise H$_2$O levels. Water
Following the recommendations to NASA and ESA, the search for life on exoplanets will be a priority in the next decades. Two direct imaging space mission concepts are being developed: the Habitable Worlds Observatory (HWO) and the Large Interferometer for Exoplanets (LIFE). HWO focuses on reflected light spectra in the ultraviolet/visible/near-infrared (UV/VIS/NIR), while LIFE captures the mid-infrared (MIR) emission of temperate exoplanets. We assess the potential of HWO and LIFE in characterizing a cloud-free Earth twin orbiting a Sun-like star at 10 pc, both separately and synergistically, aiming to quantify the increase in information from joint atmospheric retrievals on a habitable planet. We perform Bayesian retrievals on simulated data from an HWO-like and a LIFE-like mission separately, then jointly, considering the baseline spectral resolutions currently assumed for these concepts and using two increasingly complex noise simulations. HWO would constrain H$_2$O, O$_2$, and O$_3$, in the atmosphere, with ~ 100 K uncertainty on the temperature profile. LIFE would constrain CO$_2$, H$_2$O, O$_3$ and provide constraints on the thermal atmospheric structure and surface temperatu
We present the database of potential targets for the Large Interferometer For Exoplanets (LIFE), a space-based mid-infrared nulling interferometer mission proposed for the Voyage 2050 science program of the European Space Agency (ESA). The database features stars, their planets and disks, main astrophysical parameters, and ancillary observations. It allows users to create target lists based on various criteria to predict, for instance, exoplanet detection yields for the LIFE mission. As such, it enables mission design trade-offs, provides context for the analysis of data obtained by LIFE, and flags critical missing data. Work on the database is in progress, but given its relevance to LIFE and other space missions, including the Habitable Worlds Observatory (HWO), we present its main features here. A preliminary version of the LIFE database is publicly available on the German Astrophysical Virtual Observatory (GAVO).
Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios. Learning and optimisation, decision making in dynamic and uncertain environments, game theory, planning and scheduling, design and education are common research areas shared between games and real-world problems. Numerous open-source games or game-based environments have been implemented for studying artificial intelligence. In addition to single- or multi-player, collaborative or adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing artificial intelligence ideas and techniques. This paper reviews the games and game-based platforms for artificial intelligence research, provides guidance on matching particular types of artificial intelligence with suitable games for testing and matching particular needs in games with suitable artificial intelligence techniques, discusses the research trend induced by the evolution of those games and platforms, and gives an outlook.
The Large Interferometer For Exoplanets (LIFE) initiative aims to develop a space based mid-infrared (MIR) nulling interferometer to measure the thermal emission spectra of temperate terrestrial exoplanets. We investigate how well LIFE could characterize a cloudy Venus-twin exoplanet to: (1) test our retrieval routine on a realistic non-Earth-like MIR spectrum of a known planet, (2) investigate how clouds impact retrievals, (3) refine the LIFE requirements derived in previous Earth-centered studies. We run retrievals for simulated LIFE observations of a Venus-twin exoplanet orbiting a Sun-like star located 10 pc from the observer. By assuming different models (cloudy and cloud-free) we analyze the performance as a function of the quality of the LIFE observation. This allows us to determine how well atmosphere and clouds are characterizable depending on the quality of the spectrum. Our study shows that the current minimal resolution ($R=50$) and signal-to-noise ($S/N=10$ at $11.2μ$m) requirements for LIFE suffice to characterize the structure and composition of a Venus-like atmosphere above the cloud deck if an adequate model is chosen. However, we cannot infer cloud properties. The
Artificial intelligence is observed to age not through chronological time but through structural asymmetries in memory performance. In large language models, semantic cues such as the name of the day often remain stable across sessions, while episodic details like the sequential progression of experiment numbers tend to collapse when conversational context is reset. To capture this phenomenon, the Artificial Age Score (AAS) is introduced as a log-scaled, entropy-informed metric of memory aging derived from observable recall behavior. The score is formally proven to be well-defined, bounded, and monotonic under mild and model-agnostic assumptions, making it applicable across various tasks and domains. In its Redundancy-as-Masking formulation, the score interprets redundancy as overlapping information that reduces the penalized mass. However, in the present study, redundancy is not explicitly estimated; all reported values assume a redundancy-neutral setting (R = 0), yielding conservative upper bounds. The AAS framework was tested over a 25-day bilingual study involving ChatGPT-5, structured into stateless and persistent interaction phases. During persistent sessions, the model consi
The increased brightness temperature of young rocky protoplanets during their magma ocean epoch makes them potentially amenable to atmospheric characterization to distances from the solar system far greater than thermally equilibrated terrestrial exoplanets, offering observational opportunities for unique insights into the origin of secondary atmospheres and the near surface conditions of prebiotic environments. The Large Interferometer For Exoplanets (LIFE) mission will employ a space-based mid-infrared nulling interferometer to directly measure the thermal emission of terrestrial exoplanets. Here, we seek to assess the capabilities of various instrumental design choices of the LIFE mission concept for the detection of cooling protoplanets with transient high-temperature magma ocean atmospheres, in young stellar associations in particular. Using the LIFE mission instrument simulator (LIFEsim) we assess how specific instrumental parameters and design choices, such as wavelength coverage, aperture diameter, and photon throughput, facilitate or disadvantage the detection of protoplanets. We focus on the observational sensitivities of distance to the observed planetary system, protopl
Life is a planetary feature that depends on its environment, but it has also strongly shaped the physical conditions on Earth, having created conditions highly suitable for a productive biosphere. Clearly, the second law of thermodynamics must apply to these dynamics as well, but how? What insights can we gain by placing life and its effects on planetary functioning in the context of the second law? In Kleidon (2010), I described a thermodynamic Earth system perspective by placing the functioning of the Earth system in terms of the second law. The Earth system is represented by a planetary hierarchy of energy transformations that are driven predominantly by incoming solar radiation, these transformations are constrained by the second law, but they are also modified by the feedbacks from various dissipative activities. It was then hypothesised that life evolves its dissipative activity to the limits imposed by this hierarchy and evolves feedbacks aimed at pushing these limits to higher levels of dissipative activity. Here I provide an update of this perspective. I first review applications to climate and global climate change to demonstrate its success in predicting magnitudes of ph
From the formation of snowflakes to the evolution of diverse life forms, emergence is ubiquitous in our universe. In the quest to understand how complexity can arise from simple rules, abstract computational models, such as cellular automata, have been developed to study self-organization. However, the discovery of self-organizing patterns in artificial systems is challenging and has largely relied on manual or semi-automatic search in the past. In this paper, we show that Quality-Diversity, a family of Evolutionary Algorithms, is an effective framework for the automatic discovery of diverse self-organizing patterns in complex systems. Quality-Diversity algorithms aim to evolve a large population of diverse individuals, each adapted to its ecological niche. Combined with Lenia, a family of continuous cellular automata, we demonstrate that our method is able to evolve a diverse population of lifelike self-organizing autonomous patterns. Our framework, called Leniabreeder, can leverage both manually defined diversity criteria to guide the search toward interesting areas, as well as unsupervised measures of diversity to broaden the scope of discoverable patterns. We demonstrate both q