The study investigates how phenotypic changes can be encoded as low‑dimensional modulations of a self‑organizing developmental system, using neural cellular automata (NCAs) as a model. By applying low‑rank adaptation (LoRA) to pretrained NCAs, the authors show that simple rank‑one adjustments can control horizontal and vertical scaling of a 2D emoji phenotype, and that these adaptations generalize across diverse phenotypes sharing the same regulatory scaffold. Analysis of thousands of phenotype‑specific NCA adapters reveals latent low‑dimensional directions that govern scaling, style, and symmetrical fission, offering a computational realization of D’Arcy Thompson’s grid transformations in a minimal cybernetic tissue.
By Benedikt Hartl, Milton L. Montero, Marcello Barylli, Sebastian Risi, Michael Levin
arXiv:2511. 10101v2 Announce Type: replace Abstract: Background: both embodied intelligence and developmental morphogenesis depend on a division of labour between centralized guidance and distributed material dynamics, but the amount of top-down control needed to steer self-organization remains unclear.
By Takehiro Ishikawa
arXiv:2607. 10430v1 Announce Type: cross Abstract: Dimensionality reduction has proven powerful for identifying neural manifolds, which are low-dimensional structures underlying high-dimensional neural activity.
By Hardik Rajpal, Dan Goodman
The paper introduces a closed‑loop framework using autotelic reinforcement learning to explore and manipulate complex systems, specifically Lenia, a continuous cellular automaton. An agent called CARL autonomously samples diverse goals and learns a goal‑conditioned policy that intervenes with minimal, local perturbations. CARL demonstrates three key abilities: discovering stable solitons more efficiently than heuristic baselines, steering existing solitons with few interventions, and enabling humans to guide solitons through maze environments in real time via high‑level commands. The agents generalize zero‑shot to out‑of‑distribution conditions, suggesting a path toward artificial experimentalist agents that can discover and control emergent phenomena.
By Marko Cvjetko, Benedikt Hartl, Michael Levin, Cl\'ement Moulin-Frier, Pierre-Yves Oudeyer
The paper proposes a new framework for collective information engines that rely on role differentiation rather than consensus. By modeling anti‑coordination games, agents infer roles from noisy social signals tied to persistent identities, and role‑following actions reinforce those identities, creating a feedback loop that can drive collective order. The authors show that when a social loop gain—determined by identity persistence, cognitive capacity, channel fidelity, and schema strength—exceeds one, roles emerge in a bifurcation cascade whose type is selected by resource‑driven replicator dynamics, offering a mechanistic basis for distributional AGI takeoff and a control lever for platform design.
By Maximilian Puelma Touzel
arXiv:2609.17325v1 Announce Type: new
Abstract: Biological cells can be viewed as individual, interacting agents whose collective dynamics give rise to adaptive behaviour at multiple levels of organi...
By Anatoly Belikov