用可证明的框架协调多个AI智能体，让它们推理更准更快。
Reasoning-Aware Prompt Orchestration: A Foundation Model for Multi-Agent Language Model Coordination
- 用提示模板和状态向量动态管理智能体推理流程
- 实验显示推理延迟降42%，逻辑一致性提升23%
- 适合需要多智能体协作的复杂任务系统
大语言模型的兴起催生了复杂的多智能体系统，但通过提示工程协调其推理能力仍具挑战。本文提出一种理论严谨的动态提示编排框架，增强多个专业化智能体间的推理能力。该框架解决三大核心问题：智能体切换时逻辑一致性保持、推理感知的提示自适应、分布式推理的可扩展协调。方法通过提示模板、推理上下文向量和能力矩阵形式化智能体状态。理论上证明当步长满足 $α< \frac{1}{2L}$（$L$ 为状态转移函数的Lipschitz常数）时系统收敛至稳定协调模式。实现上采用分布式架构，动态路由推理任务并保持语义连贯性。在1,000个合成多智能体对话上的实验表明，推理延迟降低42%，逻辑一致性（以ROUGE-L衡量）提升23%，任务完成率高达89%且无上下文丢失。消融实验确认共识机制是主要性能驱动因素，但也发现：超过10次智能体切换后性能下降，且1,000个并发智能体需76.5GB内存。这些结果为多智能体系统中的可扩展推理提供了新范式，并建立了理解协同语言模型推理涌现的理论基础。
原文摘要 · Abstract (English)
The emergence of large language models has enabled sophisticated multi-agent systems, yet coordinating their reasoning capabilities through prompt engineering remains challenging. We present a theoretically-grounded framework for dynamic prompt orchestration that enhances reasoning across multiple specialized agents. This framework addresses three core challenges: logical consistency preservation during agent transitions, reasoning-aware prompt adaptation, and scalable coordination of distributed inference. Our approach formalizes agent states using prompt templates, reasoning context vectors, and capability matrices. We prove system convergence to stable coordination patterns when step sizes satisfy $α< \frac{1}{2L}$ where $L$ is the Lipschitz constant of the state transition function. We implement this through a distributed architecture that dynamically routes reasoning tasks while maintaining semantic coherence. Experimental results on 1,000 synthetic multi-agent conversations demonstrate a 42% reduction in reasoning latency, a 23% improvement in logical consistency measured by ROUGE-L score, and an 89% success rate for task completion without context loss across agent transitions. Ablation studies identify the consensus mechanism as the primary performance driver, while revealing limitations: performance degrades beyond 10 agent transitions, and the system requires 76.5GB memory for 1,000 concurrent agents. These findings establish a new paradigm for scalable reasoning in multi-agent systems, providing theoretical foundations for understanding reasoning emergence across coordinated language models.
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