The effectiveness of a network's response to external stimuli depends on rapid distortion-free information transfer across the network. However, the rate of information transfer, when each agent aligns with information from its network neighbors, is limited by the update rate at which each individual can sense and process information. Moreover, such neighbor-based, diffusion-type information transfer does not predict the superfluid-like information transfer during swarming maneuvers observed in nature. The main contribution of this article is to propose a novel model that uses self reinforcement, where each individual augments its neighbor-averaged information update using its previous update, to (i) increase the information-transfer rate without requiring an increased, individual update-rate; and (ii) enable superfluid-like information transfer. Simulations results of example systems show substantial improvement, more than an order of magnitude increase, in the information transfer rate, without the need to increase the update rate. Moreover, results show that the DSR approach's ability to enable superfluid-like, distortion-free information transfer results in maneuvers with smaller turn radius and improved cohesiveness.
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