Active matter flocking via predictive alignment

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 predictive alignment, in which agents predict their own future positions for each possible reorientation and adopt the orientation that maximizes a compromise between the number of neighbors and the degree of alignment with them. 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 cognitivelike dynamics.