# Reasoning-Aware Prompt Orchestration: A Foundation Model for Multi-Agent Language Model Coordination

arXiv: https://arxiv.org/abs/2510.00326 · HTML: https://mlsanity.com/paper/2510.00326/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 10 of 10)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Efficient multi-prompt evaluation of LLMs](https://mlsanity.com/paper/2405.17202/) (2024)
- [A Survey of Automatic Prompt Engineering: An Optimization Perspective](https://mlsanity.com/paper/2502.11560/) (2025)
- [Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models](https://mlsanity.com/paper/2411.00492/) (2024)
- [AMPO: Automatic Multi-Branched Prompt Optimization](https://mlsanity.com/paper/2410.08696/) (2024)
- [Prompting-based Synthetic Data Generation for Few-Shot Question Answering](https://mlsanity.com/paper/2405.09335/) (2024)
- [On the Discussion of Large Language Models: Symmetry of Agents and Interplay with Prompts](https://mlsanity.com/paper/2311.07076/) (2023)
- [MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.16730/) (2023)

## Built on by (most cited first, 1 of 1)

- [Scaling Small Agents Through Strategy Auctions](https://mlsanity.com/paper/2602.02751/) (2026)

## Abstract (arXiv)

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 < frac12L 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.