Title: Benchmarking LLMs' Swarm intelligence
Primary area: datasets and benchmarks
Keywords: Swarm intelligence, LLM, Benchmark

Abstract: Large Language Models (LLMs) show reasoning potential, but their capacity for emergent coordination in Multi-Agent Systems (MAS) under strict swarm-like constraints (e.g., limited local perception and communication) remains unexplored. Existing benchmarks often overlook the challenges of decentralized coordination with incomplete spatio-temporal information. We introduce SwarmBench, a benchmark to systematically evaluate the swarm intelligence of LLMs as decentralized agents. SwarmBench features five MAS coordination tasks (Pursuit, Synchronization, Foraging, Flocking, Transport) in a 2D grid where agents rely on local sensory input ($k\times k$ view) and local communication. We propose metrics for coordination effectiveness and analyze emergent group dynamics. Zero-shot evaluations of leading LLMs (e.g., deepseek-v3, o4-mini) reveal task-dependent performance variations. While showing rudimentary coordination, current LLMs struggle with long-range planning and adaptive strategy formation under decentralized uncertainty. Assessing LLMs under such constraints is crucial for their application in future decentralized systems. We release SwarmBench as an open, extensible toolkit with environments, prompts, evaluation scripts, and comprehensive datasets. It aims to foster research into LLM-based MAS coordination under severe informational decentralization.

TL;DR: SwarmBench evaluates LLM swarm intelligence, showing basic decentralized coordination but limited emergent complexity from local-only interactions.