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Guannan Lai

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#artificial intelligence Preprint Sep 2026

Routing Should Pay for Itself: Sparse Supervision for Economical LLM Routing

Large language model (LLM) routing reduces serving cost by assigning each query to an appropriate model while preserving response quality. Learning such a router, however, often requires executing multiple candidate models on historical queries to collect query--model quality feedback, creating a nontrivial supervision...

Guannan Lai, Ge-Lin Bian, Hao-Xuan Ma et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Pretrain Once, Route Anywhere: Towards a Foundation Model for LLM Routing

Large language model (LLM) routing aims to assign each query to the most suitable model from a heterogeneous candidate pool, improving the quality--efficiency trade-off of LLM inference. Existing routers are typically learned through local fitting: a router is optimized for a particular query workload and candidate poo...

Guannan Lai, Han-Jia Ye · 0 citations

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