Robotic swarms often cite scalability as a benefit of their deployment. To realize the full extent of this benefit, swarm behavior algorithms have to be designed so that performance is not inhibited at scale. In this work, we examine a set of persistent and adaptable swarm shape formation algorithms that are limited by onboard memory when executed at scale. We improve these algorithms by using robot communication and collective knowledge to allow robots to form persistent and adaptive shapes with a fixed memory dependence. We demonstrate the improved algorithms in both simulation and on a swarm of mobile robots.