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Limbomorphs

Alex Alvarez, Michael Levin

Year: 2026Venue: arXiv preprintArea: cs.NEType: PreprintEmbeddings: 12

Intelligence

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Abstract

Abstract:Artificial life systems are typically defined by a set of dynamical rules over an environment, an agent, or both, from which lifelike patterns may emerge. Gifbreeder is an animated version of the interactive evolutionary computation (IEC) platform Picbreeder, and was initially created to generate visual art. Instead of encoding the agent or the environment, Gifbreeder genomes encode a spatiotemporal field and evolve through the user's aesthetic selection. The evolved expressions can sometimes resemble motile lifelike creatures that we term Limbomorphs, given that they exist in a deterministic three-second looping "limbo". We assess their behavior via input-space perturbations and find species-specific reactions to different kinds of perturbations. We discuss whether these reactions may reflect goal-directed behavior like navigation, or merely the appearance of it, and more broadly how agent-like dynamics may emerge in a system with no explicitly defined agent, environment, or interaction rules.

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Limbomorphs Alex Alvarez1,∗ and Michael Levin2,3 1 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA 2 Allen Discovery Center, Tufts University, Medford, MA, USA 3 Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA ∗Corresponding author: alexrez@mit.edu Abstract Artificial life systems are typically defined by a set of dynamical rules over an environment, an agent, or both, from which lifelike patterns may emerge. Gifbreeder is an animated version of the interactive evolutionary computation (IEC) platform Picbreeder, and was initially created to generate visual art. Instead of encoding the agent or the environment, Gifbreeder genomes encode a spatiotemporal field and evolve through the user’s aesthetic selection. The evolved expressions can sometimes resemble motile lifelike creatures that we term Limbomorphs, given that they exist in a deterministic three-second looping “limbo”. We assess their behavior via input-space perturbations and find species-specific reactions to different kinds of perturbations. We discuss whether these reactions may reflect goal-directed behavior like navigation, or merely the appearance of it, and more broadly how agent-like dynamics may emerge in a system with no explicitly defined agent, environment, or interaction rules. Submission type: Late Breaking Abstract Data/Code available at: https://github.com/calvarez0/limbomorphs †©2026 Alex Alvarez and Michael Levin. Published under a Creative Commons Attribution 4.0 International (C BY 4.0) license. Gifbreeder The space of all possible three-second videos is combinatorially enormous. Most artifacts in this space are visually meaningless, and a random sample almost always returns complete noise. Picbreeder is a collaborative IEC platform in which users guide the evolution of images by repeatedly selecting aesthetically preferred variants from a population generated through CPPN-NEAT (Secretan et al., 2011; Stanley, 2007; Stanley and Miikkulainen, 2004). This allows for search over vast image space to be far more efficient, often finding recognizable patterns and objects in only a few generations. Similar ideas have been explored in IEC more broadly (Dawkins, 1987; Takagi, 2001), and more recently, it has been shown that time-indexed IEC of compositional pattern-producing networks (CPPNs) can extend static images to support the breeding of 2D and 3D animations (Tweraser et al., 2018). Gifbreeder is a web-based IEC platform inspired by Picbreeder that uses NEAT-based search (Stanley and Miikkulainen, 2002) over CPPN representations to evolve animated GIFs through user-guided selection. The chosen CPPN inputs for each pixel are its coordinates, radial distance from center, time, and a bias constant. The three outputs are interpreted and displayed as HSV channels. Formally, a genome parameterizes a CPPN Fθ(x,y,d,t,b)→(h,s,v),F_θ(x,y,d,t,b)→(h,s,v), where θ denotes the network topology, activation functions, and connection weights, x and y the pixel coordinates, d=x2+y2d= x^2+y^2, t the time step, and b the bias node. x and y inputs are normalized between −0.5-0.5 and 0.50.5 while t is normalized between 0 and 11. Phenotypes are generated by querying FθF_θ over a pixel grid and t = 45 timesteps, corresponding to 3 seconds at 15 frames per second. Besides the time input, Gifbreeder differs from Picbreeder in its set of activation functions, which were roughly chosen for their apparent ubiquity in nature (Turing, 1952; Murray, 1981; French and Brakefield, 1992; Kondo and Miura, 2010). Gifbreeder defines a spacetime field rather than a simulated world. This distinction is central to the interpretation of the results below. Limbomorphs The emergence of lifelike beings in Gifbreeder was completely unintended and unexpected since it was initially created to be an art tool. At first, Gifbreeder patterns were desolate and abstract. Occasionally and increasingly, expressions began to appear recognizable and organic. After several evolutionary runs, we began to notice expressions that exhibited apparent animacy (Heider and Simmel, 1944). Once a limbomorph was found, it could be used as a branch-off point to evolve a family of creatures. Using this technique, we quickly generated an entire families of limbomorphs with varying degrees of morphological and behavioral complexity (Figure 1). Figure 1: Nine species of the same family with increasing morphological complexity. More species can be found at https://alexrez.com/openendedness/gifbreeder/drawing.html Figure 2: Drawn strokes (black) become walls; we compute each region’s center (red, d = 0) and feed the geodesic distance from it (shading, contours) to the CPPN as the d input. Reactions to perturbations Gifbreeder genomes encode the entire GIF and not simply the limbormoph, making it impossible to excise the being from its environment. This makes assessing whether there is any ”being” at all particularly challenging. In Lenia (Chan, 2019), for example, creatures have kernels which act as a receptive field that can inform the creature about incoming obstacles (Cool et al., 2026). Limbomorphs don’t have local rules or receptors in analogous ways, however, one way to think about interacting with limbomorphs is to warp the inputs of its CPPN’s in a meaningful way. This was the motivation for the drawing tool, which warps how d is calculated (Figure 2). When the user draws walls with the perturbation tool, d is replaced by the geodesic distance from the computed center ridge. Perturbations were applied to existing expressions using this drawing tool resulting in species-specific behavioral reactions. One limbomorph species, “Fish”, is extremely avoidant to the perturbations (Figure 3a-c). This mirrors the “agnosiophobia” behavioral response recently discovered in Lenia, characterized by avoidance of regions of space where no sensory information is available. Another species, ”Caterpillar”, navigates away from the perturbation in the upper regions (Figure 3e), but towards the perturbation in lower regions (Figure 3f). Lastly, creating a ”point obstacle” in the bottom right causes the ”Jellyfish” species, which normally maintains its topology near the center of the space, to stretch and envelop the perturbation (Figure 3h). Astonishingly, when a small maze is drawn, Jellyfish orients its body around the maze (Figure 3i). Figure 3: Each limbomorph’s motion is affected differently depending on the species and the point of perturbation. Conclusion and Discussion We present Gifbreeder and its drawing tool as a new medium for studying artificial life. Species-specific reactions to input-space perturbations—like Jellyfish maintaining its topology while repositioning around the maze—suggest that behaviors suggestive of spatial competence and goal-directedness (Levin, 2019; Heylighen, 2023) can arise in a system with no explicit representation of agent, environment, or interaction rules. Could limbomorphs provide a useful model system for investigating how basal cognition can emerge from field dynamics alone? Further analysis is needed to determine the mechanisms underlying these behaviors. Acknowledgements We would like to thank Ken Stanley and Yoonsuck Choe for the helpful feedback and encouragement to pursue this idea. References B. W. Chan (2019) Lenia: biology of artificial life. Complex Systems 28 (3), p. 251–286. External Links: Document Cited by: Reactions to perturbations. J. Cool, B. Hartl, M. Levin, and S. Petti (2026) Agnosiophobia in a virtual agent: behavioral and dynamical architecture in Lenia. arXiv preprint arXiv:2605.30708. Cited by: Reactions to perturbations. R. Dawkins (1987) The blind watchmaker. W. W. Norton & Company, New York, NY. Cited by: Gifbreeder. V. French and P. M. Brakefield (1992) The development of eyespot patterns on butterfly wings. Development 116 (1), p. 103–109. Cited by: Gifbreeder. F. Heider and M. Simmel (1944) An experimental study of apparent behavior. The American Journal of Psychology 57 (2), p. 243–259. External Links: Document Cited by: Limbomorphs. F. Heylighen (2023) The meaning and origin of goal-directedness: a dynamical systems perspective. Biological Journal of the Linnean Society 139 (4), p. 370–387. External Links: Document, Link Cited by: Conclusion and Discussion. S. Kondo and T. Miura (2010) Reaction-diffusion model as a framework for understanding biological pattern formation. Science 329 (5999), p. 1616–1620. External Links: Document Cited by: Gifbreeder. M. Levin (2019) The computational boundary of a “self”: developmental bioelectricity drives multicellularity and scale-free cognition. Frontiers in Psychology 10, p. 2688. External Links: Document, Link Cited by: Conclusion and Discussion. J. D. Murray (1981) A pre-pattern formation mechanism for animal coat markings. Journal of Theoretical Biology 88 (1), p. 161–199. External Links: Document Cited by: Gifbreeder. J. Secretan, N. Beato, D. B. D’Ambrosio, A. Rodriguez, A. Campbell, J. T. Folsom-Kovarik, and K. O. Stanley (2011) Picbreeder: a case study in collaborative evolutionary exploration of design space. Evolutionary Computation 19 (3), p. 373–403. External Links: Document Cited by: Gifbreeder. K. O. Stanley and R. Miikkulainen (2002) Evolving neural networks through augmenting topologies. 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Schrum (2018) Querying across time to interactively evolve animations. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’18), New York, NY, USA. External Links: Document Cited by: Gifbreeder.

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