Qianyang Chen, Mikhail Prokopenko

Active matter generates order or patterns through nonequilibrium dynamics. An open research challenge is to determine how efficiently a nonequilibrium self-organising system can convert consumed energy into macroscopic order. We study an information-theoretic quantity that directly addresses this challenge by estimating the entropy reduction induced by a small control-parameter perturbation, relative to the generalised work required for the perturbation. This quantity has previously been considered mainly in an equilibrium or near-equilibrium context, and here we extend this framework and apply it to two nonequilibrium self-organising systems: persistent and active Ising models. We observe that the thermodynamic efficiency of nonequilibrium systems maximises at phase transitions, as in equilibrium systems. Furthermore, we compare thermodynamic efficiency and inferential efficiency across control parameters. While these two quantities are equal in equilibrium as a consequence of the fluctuation-dissipation theorem, we report that they diverge out of equilibrium, and the gap reflects how far the system is from equilibrium.

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C. Marchuk, L. Ríos, A. González, S. González, G. Pereira and C. von Lücken, “A Computational Economic Complexity Model for Regional Economic Integration: Analysis of the EU, MERCOSUR, URUPABOL, and the AndeanCommunity,” 2025 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), Valparaíso, Chile, 2025, pp. 1-8, doi: 10.1109/CHILECON66915.2025.11476476.

Regional Economic Integration is a process by which countries seek mutual benefits through the reduction of trade, social, and political barriers. This paper introduces a computational mathematical model grounded in Economic Complexity Theory to analyze economic blocs as unified entities. Four case studies are examined: the European Union, MERCOSUR, URUPABOL, and the Andean Community. Using real export data and complexity metrics, we identify the combined productive capacities of member countries. Results reveal that integration enhances product diversity and increases the ubiquity of exports within the bloc. The study demonstrates that regional integration boosts development and strengthens competitiveness in the global economy. The proposed methodological approach provides a novel tool for regional analysis and serves as a foundation for future strategies in economic cooperation and productive planning. This research contributes to understanding how collective capabilities can generate synergies that exceed individual national potentials, particularly in the context of Latin American regional development.

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Mario Franco & Carlos Gershenson
Front. Artif. Intell., Volume 9 – 2026

Nowadays, neural networks act as a synonym for artificial intelligence. Present neural network models, although remarkably powerful, are inefficient both in terms of data and energy. Several alternative forms of neural networks have been proposed to address some of these problems. Specifically, spiking neural networks are suitable for efficient hardware implementations. However, effective learning algorithms for spiking networks remain elusive, although it is suspected that effective plasticity mechanisms could alleviate the problem of data efficiency. Here, we present a new framework for spiking neural networks—Spark (https://github.com/Nogarx/Spark)—built upon the idea of modular design, from simple components to entire models. The aim of this framework is to provide an efficient and streamlined pipeline for spiking neural networks. We showcase this framework by solving the sparse-reward cartpole problem with simple plasticity mechanisms. We hope that a framework compatible with traditional ML pipelines may accelerate research in the area, specifically for continuous and unbatched learning, akin to the one animals exhibit

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