VDAR-Router: Adaptive LLMs Routing via Verbalized Query Difficulty Analysis Retrieval
Large language models are increasingly used in practical systems, making efficient model selection important for reducing deployment cost. LLM routing has emerged as a practical solution for allocating each input query to an appropriate model under a desired cost-performance trade-off. Existing routing methods often estimate model suitability from the surface semantics or embedding similarity of the input query. However, such methods may ignore the underlying difficulty of a query, leading to suboptimal routing decisions. To address the challenge, we propose VDAR-Router, a difficulty-aware retrieval-based routing framework. For each input query, VDAR-Router first generates an explicit dif...
| Authors | Yu-Chien Tang, Jun-Chen Hung, Wen-Chih Peng et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 July 2026 |
| Last updated at source | 26 August 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.