State-Dependent Routing is a mechanism within multi-agent systems that dynamically selects appropriate agent roles and model scales based on the current reasoning stage and task complexity. It optimizes computational efficiency by avoiding uniform deployment of large models, leading to significant cost reduction and improved accuracy.
State-Dependent Routing is a smart way for AI systems to choose the right size of AI model for each part of a task, instead of always using the biggest one. This makes the system much faster and cheaper to run, while also making it more accurate for complex problems.
Adaptive Model Selection, Dynamic Model Routing, Context-Aware Routing, Cognitive Demand Routing
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