Wei Wang, Niklas Hagemann,  Alejandro Gonzalez-Garcia,  Carlo Ratti, Daniela Rus

Self-reconfigurable aquatic robots offer promising potential for a wide range of marine applica-tions, including building temporary infrastructure, environmental monitoring, and on-demand transportation. However, achieving autonomous water-based self-reconfiguration, even in two di-mensions on the water surface, remains challenging, due to complex nonlinear hydrodynamics, disturbances from self-motion and neighboring robots, as well as external environmental factors. Here, we present the FloatForm platform, a group of miniature modular robotic boats, capable of self-assembling into physically connected structures, self-reconfiguring, and collectively traveling as larger assemblies via a hybrid coordination framework. Each robot unit is equipped with onboard sensing, motion control, and the ability to coordinate and physically latch with its neigh-bors. We demonstrate the feasibility of parallel self-reconfiguration, where distributed controllers on each robot handle coordination tasks such as aggregating into desired shapes and avoiding col-lisions, while a minimalist central planner oversees the overall success of each task and fixes im-perfections. This work advances the design, control, and coordination of modular robotic systems in aquatic environments, paving the way for flexible, robust and scalable applications on the water.

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Sandro M. Reia, Dieter Pfoser & Paulo R. A. Campos

The European Physical Journal B Volume 98, article number 149, (2025)

More often than not, we work in group settings where the communication structure within and between groups governs the flow of information among individuals. This structure can be designed to optimize group performance, enabling individuals to solve tasks in the shortest time or achieve the highest reward. In this paper, we explore the effects of communication patterns on the collective performance of a group of interacting agents. The agents are tasked with performing an action, where the reward depends on their skill in executing that action. At any given time, an agent switching actions has two choices: to learn from the best-performing connected agent (with probability q), or to randomly explore the action space (with probability ). Our findings indicate that decentralized networks enhance collective performance by increasing both the overall group reward and the maximum reward achieved by an individual. Conversely, in more centralized and hierarchical networks, we observe that better connected agents, as reflected by their betweenness centrality, exhibit better performance.

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Klaus Jaffe

The literature contains many contradictory conceptual descriptions of therelation between Energy and Information. Here I argue that Information is not energy.Information is a representation or description of spatiotemporal arrangements (order)of matter and energy, encoded onto a physical substrate. Known substrates includeelectromagnetic waves, material structures, chemical molecules, and neural networks—whether in brains or computers. In quantum mechanics, when information pertainsto elementary particles, the object of study and the substrate encoding the informationcoincide. This leads to view energy and information as the same phenomenon, leadingto counterintuitive and often perplexing interpretations of reality. The proposeddefinition distinguishes between thermodynamic entropy and information entropy,enabling a consilient bridge between quantum and classical mechanics, geneticinformation, human knowledge, personal models of the world, consciousness andempathy. It facilitates the study of different kinds of information—especially the kindthat generates free energy and enables useful work, as studied by infodynamics. Thecentral insight is that energy and information are distinct, irreducible properties of theuniverse. Understanding their interplay requires considering four foundationalelements: spacetime, matter, energy, and information. These clarifications arefundamental for research in natural and artificial intelligence

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Shuowen Li, Kexin Wang, Minglu Fang, Danqi Huang, Ali Asadipour, Haipeng Mi, Yitong Sun

We present a semantic feedback framework that enables natural language to guide the evolution of artificial life systems. Integrating a prompt-to-parameter encoder, a CMA-ES optimizer, and CLIP-based evaluation, the system allows user intent to modulate both visual outcomes and underlying behavioral rules. Implemented in an interactive ecosystem simulation, the framework supports prompt refinement, multi-agent interaction, and emergent rule synthesis. User studies show improved semantic alignment over manual tuning and demonstrate the system’s potential as a platform for participatory generative design and open-ended evolution.

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