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

arXiv – CS AI|Yichao Feng, Haoran Luo, Zhenghong Lin, Yiqun Sun, Pengfei Wei, Lawrence B. Hsieh, Anh Tuan Luu|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers have developed OrchMAS, a new multi-agent AI framework that uses specialized expert agents and dynamic orchestration to improve reasoning in scientific domains. The system addresses limitations of existing multi-agent frameworks by enabling flexible role allocation, prompt refinement, and heterogeneous model integration for complex scientific tasks.

Key Takeaways

→OrchMAS introduces a two-tier orchestration framework with specialized expert agents for scientific reasoning tasks.
→The system dynamically constructs domain-aware reasoning pipelines and can revise decisions based on intermediate feedback.
→The framework is model-agnostic and supports integration of different LLMs with varying capacities and costs.
→Experiments show consistent improvements over existing multi-agent systems across scientific benchmarks.
→The approach enables flexible performance-efficiency trade-offs for practical scientific AI deployments.

#multi-agent-ai #scientific-reasoning #llm-orchestration #ai-framework #research #model-agnostic #expert-systems #dynamic-planning

Read Original →via arXiv – CS AI

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