Orchestrating Intelligence: Confidence-Aware Routing for Efficient Multi-Agent Collaboration across Multi-Scale Models

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• The OI-MAS framework improves accuracy by up to 12.88% compared to baseline multi-agent systems.result 90%
• The OI-MAS framework reduces computational cost by up to 79.78% compared to baseline multi-agent systems.result 90%
• OI-MAS implements an adaptive model-selection policy across a heterogeneous pool of multi-scale LLMs.method 85%
• OI-MAS introduces a state-dependent routing mechanism that dynamically selects agent roles and model scales throughout the reasoning process.technical 85%
• The framework's performance is highly dependent on the accuracy of its confidence-aware mechanism; misjudgments in task complexity could lead to suboptimal model selection.limitation 80%
• Existing multi-agent frameworks typically deploy large language models uniformly across all agent roles, failing to account for varying cognitive demands.technical 75%
• OI-MAS uses a confidence-aware approach to decide when to employ larger, more computationally expensive models, optimizing resource use.method 80%

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