Marcus Schref, Udo Schilcher, and Christian Bettstetter
Phys. Rev. E 114, 024216 – Published 18 August, 2026

Swarmalator systems intertwine two forms of collective behavior, swarming and synchronization, leading to the emergence of specific space-time patterns. Scaling the model to real-world phenomena and technical applications is problematic due to the assumption of global coupling among all swarmalators, which is impractical under physical constraints on interaction range. Conversely, purely local coupling was shown to be infeasible. To address this gap, we introduce and evaluate the concept of multihop coupling for swarmalators, which preserves the locality of physical interactions but propagates state information throughout the network via hop-limited and probabilistic flooding. It is demonstrated that convergence to the original emergent patterns can be achieved in a reliable and fast manner while keeping overhead low. A practical guideline for selecting the range, hop limit, and forwarding probability is provided. The range required for convergence can be approximated by the connectivity threshold of random geometric graphs.

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Ariel Flint, Luca Maria Aiello, Romualdo Pastor-Satorras, and Andrea Baronchelli

PNAS August 18, 2026 123 (34) e2531697123

Large language models (LLMs) are increasingly deployed in large numbers, and their interactions make collective behavior harder to anticipate than that of a single model. While most studies compare one model with a collective of fixed size, we ask a key yet overlooked question: What is the role of group size? We show that interaction among LLMs can magnify individual biases, generate new ones, or even overturn individual preferences, and that, crucially, these effects scale in unexpected, nonlinear ways with group size. Our results demonstrate that more is different for LLM populations: The number of interacting agents is a key driver of the dynamics, with implications for the design and governance of multi-agent AI systems.

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Andrew J. King ∙ Ella G. Henry ∙ Simon Garnier ∙ William L. Allen ∙ Robert J.P. Heathcote ∙ Marco Fele ∙ Marina Papadopoulou ∙ Daniel W.E. Sankey ∙ Ines Fürtbauer

Trends in Ecology and Evolution

Collective behaviour is widespread in the animal kingdom and can enhance individual
fitness. Yet not all collective behaviours are adaptations. Instead, some may be nonadaptive
or ‘evolutionary spandrels’—traits that originated as by-products in the sense proposed
by Stephen Jay Gould and Richard Lewontin. Here, we argue that self-organising processes
provide a route through which evolutionary spandrels in collective animal behaviour
can occur, and we provide three examples: spatial organisation in primate groups,
division of labour in ants, and insect chorusing. We then consider how such outcomes
may be co-opted into adaptive roles through exaptation and conclude by outlining the
challenges associated with testing adaptive and nonadaptive hypotheses in collective
behaviour research using individual-based studies, phylogenetic comparative analyses,
and agent-based models.

Read the full article at: www.cell.com

Mohammed, Sudaff; Al-Hinkawi, Wahda Shuker; and Hasan, Nada Abdulmueen (2025) “The Role of Swarm Intelligence Systems in Shaping Urban Development Policies,” Iraqi Journal of Architecture and Planning: Vol. 24: Iss. 1, Article 3.

Swarm intelligence is a nature-inspired complex system that draws from the behaviours of social creatures such as ants and birds. This system functions through simple behavioural rules enacted by autonomous, intelligent agents. Existing literature indicates that swarm intelligence possesses a wide range of principles and characteristics derived from the theories of Biomimicry, complex adaptive systems, and parametric and generative design. While the previous studies have intensively addressed the computational aspects of intelligence, a comprehensive conceptual framework is essential for analysing complex urban forms and structures. Therefore, this research develops and applies a conceptual model of swarm intelligence by examining several projects across the following dimensions: growth strategies, mechanisms, and logic; primary and final characteristics; and the types and classifications of the system’s agents. The research emphasises the integration of theoretical and practical aspects of swarm intelligence to inform urban growth policies and promote more sustainable urban forms and structures.

Read the full article at: iqjap.uotechnology.edu.iq

Mario Franco, Zeinab Davoudmanesh, Sean P. Maley, Fernanda Sánchez-Puig, Carlos Gershenson

Seventy years of artificial intelligence are usually told as a long preamble followed by a revolution beginning around 2012. We organize the period differently, around a question the field has answered differently at different times: what kind of thing is intelligence, such that a machine could have it? Read that way, the human contribution does not withdraw as systems learn more; it relocates, and mostly to places our instruments do not record. Whether the recent acceleration is a change in kind or a change in budget is, we suspect, the more interesting question, and not one that benchmark curves can settle.

Read the full article at: www.preprints.org