VDAR-Router: Adaptive LLMs Routing via Verbalized Query Difficulty Analysis Retrieval

The paper introduces VDAR-Router, a routing framework for large language models that uses verbalized query difficulty analysis to guide model selection. It first generates an explicit difficulty profile for each query, retrieves historical examples with similar profiles, and then estimates model suitability to choose a model based on a reward function balancing performance and cost. Experiments on three datasets show that VDAR-Router consistently outperforms existing baselines in cost‑performance trade‑offs, and case studies confirm that explicit difficulty analysis improves example relevance and routing reliability.

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