REVIEW 7 cited by
Lenia - Biology of Artificial Life
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
We report a new system of artificial life called Lenia (from Latin lenis "smooth"), a two-dimensional cellular automaton with continuous space-time-state and generalized local rule. Computer simulations show that Lenia supports a great diversity of complex autonomous patterns or "lifeforms" bearing resemblance to real-world microscopic organisms. More than 400 species in 18 families have been identified, many discovered via interactive evolutionary computation. They differ from other cellular automata patterns in being geometric, metameric, fuzzy, resilient, adaptive, and rule-generic. We present basic observations of the system regarding the properties of space-time and basic settings. We provide a broad survey of the lifeforms, categorize them into a hierarchical taxonomy, and map their distribution in the parameter hyperspace. We describe their morphological structures and behavioral dynamics, propose possible mechanisms of their self-propulsion, self-organization and plasticity. Finally, we discuss how the study of Lenia would be related to biology, artificial life, and artificial intelligence.
Forward citations
Cited by 7 Pith papers
-
Replication and Information Extraction in a Minimal Agent-Environment Model
A self-labeling linear classifier can bootstrap alignment with the latent structure of Gaussian data when regularization is strong enough, and label-exchange between agents can produce collective consensus.
-
Expedition & Expansion: Leveraging Semantic Representations for Goal-Directed Exploration in Continuous Cellular Automata
A hybrid exploration algorithm that alternates semantic novelty search with VLM-generated linguistic goals discovers more diverse Flow Lenia behaviors than novelty search alone.
-
Exploring Flow-Lenia Universes with a Curiosity-driven AI Scientist: Discovering Diverse Ecosystem Dynamics
IMGEP goal exploration on simulation-wide metrics discovers more diverse Flow-Lenia ecosystem and matter-movement dynamics than random search, though key metric details and a claimed scaling study are missing.
-
A Path to Universal Neural Cellular Automata
A single neural cellular automaton rule, conditioned on a learnable hardware state, performs matrix multiplication, translation, rotation, and a block-decomposed MNIST classification.
-
Automating the Search for Artificial Life with Foundation Models
A vision-language foundation model can score simulated videos, enabling automated search for target, open-ended, and diverse artificial life simulations.
-
Adaptive Exploration in Lenia with Intrinsic Multi-Objective Ranking
In evolving Lenia patterns, ranking individuals by homeostasis, distinctiveness, and population sparsity yields slightly higher mass, variance, and compressibility than single-objective homeostasis ranking.
-
Frequency-Histogram Coarse Graining in Elementary Cellular Automata and 2D CA
A systematic atlas of frequency-histogram coarse graining for 88 elementary cellular automata rules and two 2D rules, but the appendix is corrupted and the evaluation is qualitative.
Discussion (0). Continue with ORCID to comment.