Reasoning-Aware Prompt Orchestration: A Foundation Model for Multi-Agent Language Model Coordination
Hassen Dhrif
TL;DR
The paper tackles the problem of coordinating reasoning across multiple specialized LLM agents through dynamic prompt orchestration. It introduces a formal state-space framework with agent states S_i(t) = (P_i(t), C_i(t), M_i(t)) and a global state Φ(t), coupled with a distributed consensus mechanism and an adaptive routing system to ensure scalable, coherent reasoning. The authors prove convergence under the condition $\alpha < \frac{1}{2L}$ and demonstrate substantial gains on 1,000 synthetic conversations, including a 42% reduction in reasoning latency, a 23% improvement in ROUGE-L context preservation, and an 89% task success rate without context loss; however, performance degrades beyond roughly 10 agent handoffs and demands significant memory (about 76.5 GB for 1,000 agents), indicating fundamental limits and the need for hierarchical or hybrid coordination in practice. These results establish a theoretical and empirical foundation for scalable reasoning in multi-agent LLM systems and point toward architectures that go beyond prompt engineering to address distributed cognition challenges in large-scale deployments.
Abstract
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.