标题： 活性物质通过预测性对齐形成群体 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现象（如鸟群和鱼群）仍然是物理学中的一个重大挑战。对齐相互作用对于群体行为至关重要，然而单独依靠这些相互作用通常被认为不足以抵抗噪声来维持群体的凝聚力，这迫使传统模型依赖于人工边界或额外的吸引力。在这里，我们报告了一个首次利用纯对齐相互作用实现凝聚群体行为的模型，引入了预测性对齐：代理根据邻居预测的未来朝向来定向。该方法在一个离散时间的 Vicsek 型框架中实现，提供了稳健且抗噪的凝聚力，而无需额外参数。在稳定状态下，群体规模与相互作用半径呈线性关系，对噪声或推进速度几乎免疫，并且在存在噪声的情况下，群体能够连贯地跟随领导者。这些发现揭示了预测策略如何增强自组织能力，为一种新的活性物质模型铺平了道路，这种模型融合了物理和类似认知的动力学。 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. Alignment interactions are essential for flocking, yet alone, they are generally considered insufficient to maintain cohesion against noise, forcing traditional models to rely on artificial boundaries or added attractive forces. Here, we report the first model to achieve cohesive flocking using purely alignment interactions, introducing predictive alignment: agents orient based on the predicted future headings of their 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.