标题： 通过预测对齐的活性物质群集 显示英文标题

标题： Active Matter Flocking via Predictive Alignment

摘要： 理解活性物质中的集体自组织，例如鸟群和鱼群，仍然是物理学中的一个重大挑战。 诱导对齐的相互作用对于群体形成是必不可少的；然而，仅靠对齐通常不足以在噪声存在的情况下维持群体的凝聚力，导致传统模型引入人工边界或显式吸引力。 在此，我们提出了一种模型，通过引入预测性对齐，仅依靠对齐相互作用实现稳定的群体形成，其中个体重新定向以最大化与预期未来邻居的主要方向的一致性。 在离散时间Vicsek型框架中实现，这种方法在不增加参数的情况下提供了稳健、抗噪声的凝聚力。 在稳定区域，群体大小与相互作用半径呈线性关系，几乎不受噪声或推进速度的影响，并且在噪声下群体能够协调地跟随领导者。 这些发现揭示了预测策略如何增强自组织，为融合物理和类似认知动力学的新一类活性物质模型铺平了道路。 显示英文摘要

摘要： Understanding collective self-organization in active matter, such as bird flocks and fish schools, remains a grand challenge in physics. Interactions that induce alignment are essential for flocking; however, alignment alone is generally insufficient to maintain group cohesion in the presence of noise, leading traditional models to introduce artificial boundaries or explicit attractive forces. Here, we propose a model that achieves cohesive flocking through purely alignment-based interactions by introducing predictive alignment, in which agents reorient to maximize alignment with the prevailing orientations of their anticipated future neighbors. Implemented in a discrete-time Vicsek-type framework, this approach delivers robust, noise-resistant cohesion without additional parameters. In the stable regime, flock size scales linearly with interaction radius, remaining nearly immune to noise or propulsion speed, and the group coherently follows a leader under noise. These findings reveal how predictive strategies enhance self-organization, paving the way for a new class of active matter models blending physics and cognitive-like dynamics.