PEAR: Progressive Evidence-Based AutoResearch for Industrial Search Systems
AutoResearch improves systems through iterative experimentation: agents propose candidate modifications, evaluate them, and use the results to guide subsequent exploration. Applying this paradigm to industrial search presents two challenges. (1) Common AutoResearch approaches follow a keep-if-better rule, retaining the highest-scoring candidate for subsequent experiments. Under non-stationary traffic, transient gains may be mistaken for persistent improvements, impairing reliable accumulation of search knowledge. (2) Candidate modifications can be evaluated at multiple fidelity levels, from low-cost proxies to online validation, differing in cost, objective alignment, and statistical reli...
| Authors | Yifan Wang, Shipeng Zhu, Fei Xiong et al. |
|---|---|
| Primary category | cs.IR |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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