--Swarm intelligence describes how simple, decentralized agents can collectively produce complex behaviors. Recently, the concept of swarming has been extended to large language model (LLM)-powered systems, such as OpenAI's Swarm (OAS) framework, where agents coordinate through natural language prompts. Using OAS, we implement and compare classical and LLMbased versions of two well-established swarm algorithms: Boids and Ant Colony Optimization. Results indicate that while LLMpowered swarms can emulate swarm-like dynamics, they are constrained by substantial computational overhead. For instance, our LLM-based Boids simulation required roughly 300 more computation time than its classical counterpart, highlighting current limitations in applying LLM-driven swarms to real-time systems. W ARM intelligence continues to attract significant attention from researchers and engineers. In nature, swarming systems exist as flocks of birds, schools of fish, and colonies of ants, where they are characterized by local interactions among agents following simple rules. These interactions give rise to global patterns and adaptive behaviors that are greater than the sum of their parts [1]. However, the term "swarm" has recently been appropriated in novel contexts, such as OpenAI's Swarm (OAS) framework [2], where the dynamics and mechanisms differ from their traditional counterparts. This paper explores the differences, examining how the principles that define classical swarm algorithms translate, or fail to translate, within large language model (LLM)-based systems such as OAS, which is selected as a representative framework for LLM-powered swarms in this paper.

When it comes to expensive black-box optimization problems, Bayesian Optimization (BO) is a well-known and powerful solution. Many real-world applications involve a large number of dimensions, hence scaling BO to high dimension is of much interest. However, state-of-the-art high-dimensional BO methods still suffer from the curse of dimensionality, highlighting the need for further improvements. In this work, we introduce BOIDS, a novel high-dimensional BO algorithm that guides optimization by a sequence of one-dimensional direction lines using a novel tailored line-based optimization procedure. To improve the efficiency, we also propose an adaptive selection technique to identify most optimal lines for each round of line-based optimization. Additionally, we incorporate a subspace embedding technique for better scaling to high-dimensional spaces. We further provide theoretical analysis of our proposed method to analyze its convergence property. Our extensive experimental results show that BOIDS outperforms state-of-the-art baselines on various synthetic and real-world benchmark problems.

Understanding the mechanisms behind mysterious evolutionary This simulation revolves around two processes: transitions in individuality (ETIs) is a central narrative self-organization, which is governed by evolving neural networks in contemporary biology Okasha (2005); Szathmáry that dictate boid behaviour, and natural selection, (2015). These transitions, which encompass the evolutionary which forces these agents to adapt and survive. This subtle milestones enabling discrete biological entities to coalesce interplay between individual behaviour modulation and into complex, higher-order wholes, pose profound group dynamics results in the formation of cohesive groups questions about the origins of collective reproduction and capable of collective reproduction--a phenomenon that mirrors complex life forms. At the heart of understanding ETIs lies key aspects of ETIs. VitaNova demonstrates how the the exploration of how new levels of biological organisation combined forces of self-organization and natural selection emerge and the dynamics by which these levels attain and can drive the spontaneous formation of reproductive groups, sustain the capability for collective reproduction Smith and providing new insights into the evolution of complex biological Szathmary (1997).

We present a hydrodynamic simulation system using the GPU compute shaders of DirectX for simulating virtual agent behaviors and navigation inside a smoothed particle hydrodynamical (SPH) fluid environment with real-time water mesh surface reconstruction. The current SPH literature includes interactions between SPH and heterogeneous meshes but seldom involves interactions between SPH and virtual boid agents. The contribution of the system lies in the combination of the parallel smoothed particle hydrodynamics model with the distributed boid model of virtual agents to enable agents to interact with fluids. The agents based on the boid algorithm influence the motion of SPH fluid particles, and the forces from the SPH algorithm affect the movement of the boids. To enable realistic fluid rendering and simulation in a particle-based system, it is essential to construct a mesh from the particle attributes. Our system also contributes to the surface reconstruction aspect of the pipeline, in which we performed a set of experiments with the parallel marching cubes algorithm per frame for constructing the mesh from the fluid particles in a real-time compute and memory-intensive application, producing a wide range of triangle configurations. We also demonstrate that our system is versatile enough for reinforced robotic agents instead of boid agents to interact with the fluid environment for underwater navigation and remote control engineering purposes.

Swarm intelligence is a natural step in the evolution of certain social species. It explains why ants colonize, bees swarm, fish school and birds flock. Nature has proven that when individual creatures collaboratively work and think together as unified systems toward a common goal, they're more likely to reach that goal faster and more accurately than if they were to attempt it individually. In other words, they're smarter together than they are on their own. Swarm intelligence is the collective behavior of decentralized, self-organized systems (natural or artificial) that can maneuver quickly in a coordinated fashion. In nature, this closed-loop, collaborative behavior is unique within each species.

In my previous blog post talking about our project, I examined how our Ekho Collective's process of building our immersive installation Laila has changed during COVID-19. Now our project is almost finished, and the tickets are on sale for showings in August 2020 in Helsinki. Here, I've collected thoughts from members of our collective on the process of building Laila. Joonas Nissinen is our creative technologist with a background in computer graphics and artificial intelligence. When creating an operatic experience, we need to have a traditional plot and narrative.

In nature, flocking or swarm behavior is observed in many species as it has beneficial properties like reducing the probability of being caught by a predator. In this paper, we propose SELFish (Swarm Emergent Learning Fish), an approach with multiple autonomous agents which can freely move in a continuous space with the objective to avoid being caught by a present predator. The predator has the property that it might get distracted by multiple possible preys in its vicinity. We show that this property in interaction with self-interested agents which are trained with reinforcement learning to solely survive as long as possible leads to flocking behavior similar to Boids, a common simulation for flocking behavior. Furthermore we present interesting insights in the swarming behavior and in the process of agents being caught in our modeled environment.

We pick the cell size to be bounded by (r)/( (n)), so that each grid cell will contain at most one sample, and thus the grid can be implemented as a simple n-dimensional array of integers: the default 1 indicates no sample, a non-negative integer gives the index of the sample located in a cell. Step 1. Select the initial sample, x0, randomly chosen uniformly from the domain.