Positional task conditioning for scalable defect detection across product families in large product catalogs

2026-09-08

AI Summary

本文通过分解检测任务和引入位置任务条件（PTC）方法，解决了大型产品目录中产品家族的不一致性问题，提高了缺陷检测的准确性和可扩展性。

Abstract

Product families in large product catalogs suffer from inconsistencies such as duplicates and unit mismatches that degrade customer experience. Detecting these requires reasoning over multiple error types across lengthy product listings, where LLM classification quality degrades due to long-context limitations. We address this by decomposing detection into focused sub-tasks that reduce context and isolate error types, improving F1 from 52\% to 87\%. For scalable deployment, we introduce Positional Task Conditioning (PTC), which distills this capability into a single smaller model by reinforcing task identity at structural prompt boundaries. PTC outperforms rationale-based distillation across five models and two architecture families, achieving within 1.79\% F1 of the frontier at upto 98\% lower cost. Our system is deployed across multiple countries processing 10+ million product families.

Problem

• product families
• inconsistencies
• duplicates
• unit mismatches
• customer experience

Innovation

• Positional Task Conditioning
• Error Detection
• Scalability
• Cost Efficiency