Lenia Swarm
Flow Lenia: perturbation experiments that test whether spatially localized patterns cohere, recover, and show goal-directedness, and cartography of the genotype-phenotype morphospace they live in.
Flow Lenia generalizes cellular automata to continuous state, time, and space. Two functions define its dynamics: a kernel K for neighborhood sensing and a growth function G for updates over time. Every persistent structure emerges from those two, from gliders and walkers up to self-replicators. The flow variant we use conserves mass for these spatially localized patterns (SLPs) and allows local, position-dependent rules, which is what makes multi-species evolution possible.
Because every rule is known and cheap to rerun, this is a clean place to study morphogenesis and emergence. We grow a creature, perturb it, and watch whether it holds together, the same competency-under-stress question Levin's TAME framing asks of living matter. And because a creature's genotype is just its rule parameters, we can map the whole space of forms instead of studying one creature at a time.
The perturbation harness matches control-versus-intervention pairs across two environments, flat and high-theta, with three families. init_state re-seeds the initial mass placement, flow_regime halves the integration timestep, and param_noise re-rolls the growth function, holding architecture fixed but resampling rule parameters. Recovery falls off across them: init_state perturbations leave the trajectory intact in nearly all cases, flow_regime recovers in some, and param_noise in few.
A related question, prompted by Cool et al. 2026, is whether a creature's position in the morphospace predicts how perturbation couples to its heading. Three of their four Chan-catalog creatures swerve around occluded regions that carry no sensory information while the fourth dies on contact, and they find the difference is hardwired per creature without saying which rules cause it. We have thousands of creatures with their rules attached, so we can ask whether the swervers cluster on our map.
The morphospace work asks a different question from creature discovery. Instead of looking for beautiful specimens, we ask whether Flow Lenia's genotype-to-phenotype map has measurable topology, and how that synthetic cloud of shapes sits next to developmental and biological ones. So we put Flow Lenia specimens, developmental-model snapshots, and biological outlines into one shared descriptor layer and ask the same things of each. Topological data analysis finds loop structure. Local cohort comparison tells us which Lenia neighborhoods lean toward developmental or biological shapes. Transport then walks around a loop and checks whether the state returns with its phenotype, or whether residue is left behind in the rule layer.
Repository path: dossiers/lenia-swarmThe compendium is the materialized morphospace. 13,390 indexed creatures, 7,664 stable under standard initial conditions, spread across 698 species in 8 families. Each row ties a genotype to its phenotype, provenance, and a replay config.
The system is a controller-worker topology over a SWIM gossip cluster, with an MLX Swift engine for GPU-accelerated Flow Lenia across Apple machines, split into three components:
LeniaCore: physics engine, distributed actors, search infrastructure.LeniaCLI: headless workflows underdiscover,orchestrate,index,analyze,intervene,publish.LeniaStudio: macOS SwiftUI app for local exploration, host mode, and worker mode.