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Informational Memory Shapes Collective Behavior in Intelligent Swarms
Shengkai Li1, Trung V Phan2, Luca Di Carlo1
1Princeton University, Department of Physics, Princeton, New Jersey 08544, USA.
Physical Review Letters
|April 17, 2026
Summary
We studied 2-D drone swarms where agents process information from past collisions. This memory-driven computation leads to emergent collective behaviors and phase transitions in spin symmetry.
Area of Science:
- Physics of complex systems
- Robotics and autonomous systems
- Information theory and computation
Background:
- Collective behavior in swarms often relies on direct interactions.
- The role of internal agent memory and information processing in collective organization is less understood.
- Understanding how information dynamics influence emergent phenomena is crucial.
Purpose of the Study:
- To investigate how agent memory and information processing drive collective behavior in 2-D swarms.
- To explore the emergence of novel collective states and phase transitions.
- To develop a theoretical framework linking information processing to swarm organization.
Main Methods:
- Experimental study of air-table drones with internal memory and binary decision variables.
- Theoretical modeling using an effective potential landscape approach.
- Analysis of history-dependent feedback and its impact on collective dynamics.
Main Results:
- Observed spontaneous spin polarization and bifurcated spin collectives.
- Discovered a memory-induced phase transition that breaks population-level spin symmetry.
- Demonstrated that temporally correlated computation can drive collective organization.
Conclusions:
- Physically interacting drone swarms serve as a model for informational ensembles.
- Emergent behavior arises from the interplay of physical interaction and information processing.
- Information processing can act as a primary driver for collective organization, complementing mechanical coupling.
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