How Often Should a Recommender Call an LLM? Value-Weighted Routing, Monitoring, and Seasonal Robustness
Routing decisions between a cheap heuristic and an expensive large language model (LLM) are typically framed as a difficulty problem: send the hard cases to the expensive path. We argue this framing is incomplete because difficulty and business value are distinct axes - a difficult cheap item and a difficult costly item do not have the same cost of error. We present Value Router, a fully synthetic simulation of a retail merchandising pipeline that routes items using only estimated difficulty and estimated value, never ground truth. The study has three stages. First, a value-weighted threshold router is compared with a difficulty-only and a random baseline on a synthetic catalog with an in...
| Authors | Bhavtosh Rath |
|---|---|
| Primary category | cs.AI |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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