AI RESEARCH
A Unified Structured Query Understanding Framework for Industrial Semantic Search
arXiv CS.LG
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ArXi:2605.27441v1 Announce Type: cross Query understanding in large-scale industrial search systems is typically implemented as a cascade of disparate, task-specific components. While individually optimizable, this fragmented architecture incurs high maintenance overhead and results in inconsistent behaviors, particularly for long-tail queries. In this work, we propose and deploy a unified structured query understanding system that consolidates these heterogeneous functions into a single Small Language Model (SLM) that performs schema-constrained generation.