OrchMAS: Orchestrated Reasoning with Multi Collaborative Heterogeneous Scientific Expert Structured Agents

2026-03-03

AI Summary

Current scientific multi-agent systems are constrained by static prompts, fixed roles, and homogeneous models, limiting their capacity to handle complex, long-horizon scientific reasoning tasks and lacking dynamic error-correction capabilities. This work proposes a two-layer interactive multi-model collaboration framework: an upper orchestration layer dynamically constructs domain-aware reasoning pipelines and instantiates heterogeneous expert agents, while a lower execution layer carries out task steps using role- and context-aware prompting, enabling feedback-driven iterative refinement. The framework introduces, for the first time, a dynamically reconfigurable heterogeneous collaboration mechanism that closes the loop among role assignment, prompt optimization, and workflow replanning. This approach substantially enhances the robustness, flexibility, and specialization of scientific reasoning, outperforming state-of-the-art methods across multiple scientific reasoning benchmarks.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Multi-agent large language model frameworks are promising for complex multi step reasoning, yet existing systems remain weak for scientific and knowledge intensive domains due to static prompts and agent roles, rigid workflows, and homogeneous model reliance, leading to poor domain adaptation, limited reasoning flexibility, and high latency on heterogeneous or long-horizon scientific tasks. They also struggle to revise earlier decisions when intermediate reasoning diverges, reducing reliability in structured and calculation heavy settings. To address these limitations, we propose a scientific domain oriented interactive two tier multi model orchestration framework. A dedicated orchestration model analyzes each task, dynamically constructs a domain aware reasoning pipeline, and instantiates specialized expert agents with tailored prompts, while an execution model performs each step under generated role and instruction specifications. The orchestrator iteratively updates the pipeline based on intermediate feedback, enabling dynamic replanning, role reallocation, and prompt refinement across multi turn interactions, strengthening robustness and specialization for scientific reasoning through structured heterogeneous model collaboration. The framework is model agnostic and supports heterogeneous LLM integration with different capacities or costs, enabling flexible performance efficiency trade offs in practical scientific deployments. Experiments show consistent improvements over existing multi agent systems and strong baselines across diverse reasoning and scientific style benchmarks.

Problem

• multi-agent systems
• scientific reasoning
• heterogeneous models
• dynamic orchestration
• domain adaptation

Innovation

• orchestrated reasoning
• heterogeneous multi-agent systems
• dynamic pipeline construction
• scientific reasoning
• model-agnostic framework