AI Research
COMED: The Missing Middle Between Routing and Collaboration in Multi-LLM Inference
A new framework for dynamic, conditional multi-model inference in Large Language Models.
No single Large Language Model (LLM) is uniformly reliable across queries, motivating multi-model inference systems that either route among models or combine their outputs. However, routing stops after selecting an initial model, while dense collaboration invokes peers on every q...
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ArXiv CS.CL
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ArXiv CS.CL
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COMED bridges the gap between inefficient routing and resource-heavy collaboration by enabling conditional model interaction. This approach optimizes latency and performance by selectively engaging peer models only when the primary model expresses uncertainty.
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