Scalable Prompt Routing via Fine-Grained Latent Task Discovery
Prompt routing dynamically selects the most appropriate large language model from a pool of candidates for each query, optimizing performance while managing costs. As model pools scale to include dozens of frontier models with narrow performance gaps, existing approaches face significant challenges: manually defined task taxonomies cannot capture fine-grained capability distinctions, while monolithic routers struggle to differentiate subtle differences across diverse tasks. We propose a two-stage routing architecture that addresses these limitations through automated fine-grained task discovery and task-aware quality estimation. Our first stage employs graph-based clustering to discover l...
| Authors | Yunyi Zhang, Soji Adeshina, Sheng Guan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 March 2026 |
| Last updated at source | 23 March 2026 |
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