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
arXiv:2507. 03065v2 Announce Type: replace Abstract: Why do some macroscopic structures remain identifiable even though their microscopic constituents continually change?
By Xin Li
arXiv:2609.01170v1 Announce Type: new
Abstract: Large language models exhibit a modular internal organization that mirrors well-studied functional networks of the human brain, but how this organizati...
By Guangqi Li, Yongxin Li
arXiv:2605. 13438v3 Announce Type: replace Abstract: Existing agent memory remains predominantly reactive and retrieval-based, lacking the capacity to autonomously organize experience into persistent cognitive structure.
By Suli Wang, Yiqun Duan, Yu Deng, Rundong Zhao, Dai Shi, Minghua Deng, Chen Chen, Xinliang Zhou
arXiv:2607. 07743v1 Announce Type: cross Abstract: Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information.
By Meet Barot, Daniel Berenberg, Sina Khajehabdollahi
Autoregressive transformers trained on limited trajectories of nonlinear dynamical systems can extrapolate to unseen parameter regimes, reproducing period-doubling cascades, chaotic dynamics, and attractor structures with high fidelity. In the logistic map, the model captures successive period doublings up to period 128, achieving a scaling ratio within $5 imes10^{-4}$ of the Feigenbaum constant. The study also shows how control‑parameter information is processed via attention, shaping the closed‑loop dynamics during training.
By Yilun Liu, Yi Zhang, Ganyu Wu, Sikuan Yan, Mengyue Wang, Alois Knoll, Volker Tresp, Yunpu Ma
Agency is often invoked in research on philosophy, biology, and cognitive science without a clear account of how it originates from material organization. Building on temporally parametrized (F, A)-systems, this paper develops a graded organizational theory of agency grounded in relational biology, physical biosemiotics, and process ontology.