Emergent Coordination in Multi-Agent Systems via Pressure Fields and Temporal Decay

TL;DR

This paper introduces a novel multi-agent coordination method using pressure fields and temporal decay, which outperforms traditional hierarchical and conversation-based approaches in scheduling tasks, demonstrating scalability and robustness.

Contribution

The paper presents a new decentralized coordination paradigm inspired by natural mechanisms, formalizes it mathematically, and empirically validates its superior performance over existing methods.

Findings

Pressure-field coordination achieves 48.5% solve rate in scheduling tasks.

Temporal decay is crucial, reducing solve rate by 10% when disabled.

Method maintains high performance across 1 to 4 agents.

Abstract

Current multi-agent LLM frameworks rely on explicit orchestration patterns borrowed from human organizational structures: planners delegate to executors, managers coordinate workers, and hierarchical control flow governs agent interactions. These approaches suffer from coordination overhead that scales poorly with agent count and task complexity. We propose a fundamentally different paradigm inspired by natural coordination mechanisms: agents operate locally on a shared artifact, guided only by pressure gradients derived from measurable quality signals, with temporal decay preventing premature convergence. We formalize this as optimization over a pressure landscape and prove convergence guarantees under mild conditions. Empirically, on meeting room scheduling across 1,350 trials, pressure-field coordination outperforms all baselines: 48.5% aggregate solve rate versus 12.6% for conversation-based coordination, 1.5% for hierarchical control, and 0.4% for sequential and random baselines (all pairwise comparisons p < 0.001). Temporal decay is essential: disabling it reduces solve rate by 10 percentage points. On easy problems, pressure-field achieves 86.7% solve rate. The approach maintains consistent performance from 1 to 4 agents. Implicit coordination through shared pressure gradients outperforms explicit hierarchical control, suggesting that constraint-driven emergence offers a simpler and more effective foundation for multi-agent AI.