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 Latin Square constraint satisfaction across 1,078 trials, pressure-field coordination matches hierarchical control (38.2% vs 38.8% aggregate solve rate, p=0.94, indicating statistical equivalence). Both significantly outperform sequential (23.3%), random (11.7%), and conversation-based multi-agent dialogue (8.6%, p<0.00001). Temporal decay is essential: disabling it increases final pressure 49-fold (d=4.15). On easy problems, pressure-field achieves 87% solve rate. The approach maintains consistent performance from 2 to 32 agents. Our key finding: implicit coordination through shared pressure gradients achieves parity with explicit hierarchical control while dramatically outperforming explicit dialogue-based coordination. This suggests that constraint-driven emergence offers a simpler, equally effective foundation for multi-agent AI.

翻译：当前的多智能体大语言模型框架依赖于从人类组织结构中借鉴的显式编排模式：规划者向执行者分派任务，管理者协调工作者，层级控制流主导智能体间的交互。这些方法存在协调开销问题，且随智能体数量与任务复杂度的增加而难以扩展。受自然协调机制启发，我们提出一种根本不同的范式：智能体仅基于可测量质量信号导出的压力梯度引导，在共享工作对象上进行局部操作，并通过时间衰减机制避免过早收敛。我们将此形式化为压力场上的优化问题，并在温和条件下证明了收敛性保证。在拉丁方约束满足问题的1,078次实验验证中，压力场协调与层级控制表现相当（总体解决率38.2%对比38.8%，p=0.94，表明统计等效性）。两者均显著优于顺序策略（23.3%）、随机策略（11.7%）以及基于对话的多智能体协调（8.6%，p<0.00001）。时间衰减机制至关重要：禁用该机制会使最终压力增加49倍（d=4.15）。在简单问题上，压力场方法达到87%的解决率。该方法在2至32个智能体规模下均保持稳定性能。我们的核心发现是：通过共享压力梯度实现的隐式协调，在达到与显式层级控制同等效能的同时，显著优于基于显式对话的协调方式。这表明约束驱动的涌现机制为多智能体人工智能提供了一种更简洁且同样有效的基础框架。