Lenia Swarm

1. Read the Lenia docs map.
2. Open the Compendium Inspector to browse published creatures. For schema, taxonomy, and artifact details, use the schema and taxonomy docs.

• March 1, 2026
  Lenia cabinet docs rebuilt with current contracts, internals, and universe lanes.
• February 3, 2026
  Taxonomy/ecology/morphometrics synthesis reflected in docs and generated analysis notes.
• January 20, 2026
  IMGEP and activity-tracking surfaces were documented in generated implementation analysis.

Flow Lenia generalizes cellular automata to continuous state, time, and space. Two functions define the dynamics: a kernel K for neighborhood sensing and a growth function G for updates. Every persistent structure emerges from those two, from gliders and walkers up to self-replicators. The flow variant adds mass-conserving transport, so a creature stays a coherent object rather than a wave of growth and decay. We use it to search parameter space and index what stays alive.

For the mechanics with interactive widgets, see A Primer on Lenia.

Under perturbation, a Flow Lenia creature either returns to its baseline trajectory or settles into a different attractor. Each trial runs matched control-versus-intervention pairs across two environments, flat and high-theta, with three perturbation families. init_state re-seeds the initial mass placement. flow_regime halves the integration timestep. param_noise re-rolls the growth function, holding architecture fixed but resampling rule parameters.

Recovery is graded across the families. init_state perturbations leave the trajectory intact in nearly all cases. flow_regime perturbations recover in some. param_noise perturbations recover in few, and the failed recoveries sit far from baseline.

The morphospace track is separate from the creature-discovery track. Here the question is not whether every specimen looks beautiful; it is whether Flow Lenia's genotype-to-phenotype map has measurable topology, how that synthetic cloud sits near developmental and biological shape clouds, and where loop transport leaves residue.

What the measurements say:

• Shared 12-axis shape-descriptor space now covers 25,167 Flow Lenia specimens, 859 EmbryoMaker morphology snapshots, and 232 Dryad fish body outlines.
• On the full Lenia cloud, the 4,096-landmark TDA run finds 4,930 H1 intervals, with top persistence 2.78. On the no-food 8,192 cohort, exact dense TDA finds 7,275 H1 intervals, with top persistence 0.80.
• EmbryoMaker morphology snapshots have 297 exact H1 intervals with top persistence 0.29. The Dryad fish-outline cohort has 66 exact H1 intervals with top persistence 0.64. The interval counts are sample-size dependent; the comparable fact is that the same descriptor layer sees loop structure in all three clouds.
• The best local result is fish-near: an H1 neighborhood near the Dryad outline cohort, with persistence 0.563, Dryad-outline median distance 3.30 against a global Dryad-outline median of 5.90, and a stratified transport-enrichment p-value around 0.0002.
• Transport gives us the curvature test: move around a phenotype neighborhood, return near the same form, and measure the residue left in state. The broad corpus has 4,802 flow-supported transport groups and a real transport tail, with the fish-near neighborhood carrying the strongest localized signal so far.

Fiber Theory keeps the geometry, topology, and comparison data in one place.

Controller-worker topology over a SWIM gossip cluster, with an MLX Swift engine for GPU-accelerated Flow Lenia across Apple machines. Worker failures are detected automatically and pending jobs requeued.

• LeniaCore: physics engine, distributed actors, search infrastructure.
• LeniaCLI: headless workflows under discover, orchestrate, index, analyze, intervene, publish.
• LeniaStudio: macOS SwiftUI app for local exploration, host mode, and worker mode.

For deeper internals see the Universe Primer and the cabinet docs.