Revisiting Boids for Emergent Intelligence via Multi-Agent Collaborative Tool-Building

Xisen Wang, Qi Zhang

Keywords: Multi Agent, Boids, Emergent Intelligence, Tool Building, Large Language Model, Local Rules

TL;DR: This work reframes collaborative tool building as a controlled testbed for emergent intelligence, showing how simple Boids-style coordination rules can transform local interactions among LLM agents into structured, evolving collective behavior.

Abstract: Can LLM-based agents exhibit emergent intelligence when governed only by simple, local rules—without predefined workflows or central coordination? We explore this question by extending the classical Boids framework from physical flocking to cognitive collaboration, using it to study how multi-agent systems spontaneously organize around tool creation. Unlike prior work that treats tool building merely as a vehicle for downstream task improvement, we frame it as a lens for understanding how decentralized interaction gives rise to coordination, specialization, and long-horizon adaptation. Each agent follows an observe–reflect–build loop within a shared environment, guided only by three Boids-inspired primitives: separation, alignment, and cohesion. Through these minimal rules, agents collectively invent, adopt, and refine tools. Our results show that Boids-style coordination sustains long-horizon exploration and diversity in open-ended domains, supporting the continuous accumulation of structural and functional complexity beyond what uncoordinated baselines achieve. Our contributions are twofold: (1) an end-to-end infrastructure and metrics for collective tool building as a sandbox for emergent intelligence; and (2) a Boids-inspired algorithm that demonstrates how simple local rules can trigger complex collaborative dynamics and long-horizon complexity growth.

Archival Option: The authors of this submission do *not* want it to appear in the archival proceedings.

Submission Number: 61