Value Router: Optimizing LLM Calls with Value-Weighted Routing
A recent study published on arXiv (2607.25068) presents Value Router, a synthetic simulation designed for making routing choices between cost-effective heuristics and costly large language models (LLMs) within a retail merchandising framework. The researchers contend that conventional difficulty-based routing fails to account for the separate dimensions of business value and difficulty. Value Router relies on estimated values and difficulties rather than actual data. In the initial phase, a value-weighted threshold router is assessed against difficulty-only and random baselines using a synthetic catalog that exhibits an inverse relationship between category volume and value. The value-weighted approach achieves a recall of true high-value items (60%) comparable to the difficulty-only baseline, while significantly enhancing precision (98.3% compared to 94.3%). Additionally, the research features a decision logger and monitor for seasonal resilience.
Key facts
- Paper on arXiv: 2607.25068
- Title: How Often Should a Recommender Call an LLM? Value-Weighted Routing, Monitoring, and Seasonal Robustness
- Introduces Value Router, a synthetic simulation for routing decisions
- Compares value-weighted threshold router with difficulty-only and random baselines
- Value-weighting achieves 98.3% precision vs 94.3% for difficulty-only
- Recall of true high-value items is 60% for both value-weighted and difficulty-only
- Uses estimated difficulty and estimated value, never ground truth
- Includes decision logger and monitor for seasonal robustness
Entities
Institutions
- arXiv