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.

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：规划者向执行者委派任务，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，其随智能体数量与任务复杂度的增加而扩展性不佳。我们提出一种受自然协调机制启发的根本性不同范式：智能体仅基于可测量质量信号衍生的压力梯度引导，在共享工件上进行局部操作，同时通过时间衰减机制防止过早收敛。我们将此形式化为压力景观上的优化问题，并在温和条件下证明了收敛性保证。在涵盖1,350次试验的会议室调度任务中，压力场协调方法在实证上优于所有基线：总体解决率达到48.5%，而基于对话的协调为12.6%，层级控制为1.5%，顺序与随机基线为0.4%（所有成对比较p < 0.001）。时间衰减机制至关重要：禁用该机制会使解决率降低10个百分点。在简单问题上，压力场方法达到86.7%的解决率。该方法在1至4个智能体规模下均保持稳定性能。通过共享压力梯度实现的隐式协调优于显式层级控制，表明约束驱动的涌现机制为多智能体人工智能提供了更简洁高效的基础框架。