[2603.01486] Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study

Computer Science > Artificial Intelligence arXiv:2603.01486 (cs) [Submitted on 2 Mar 2026] Title:Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study Authors:Emmanuel Aboah Boateng, Kyle MacDonald, Akshad Viswanathan, Sudeep Das View a PDF of the paper titled Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study, by Emmanuel Aboah Boateng and 3 other authors View PDF HTML (experimental) Abstract:Accurately mapping user queries to business categories is a fundamental Information Retrieval challenge for multi-category marketplaces, where context-sparse queries such as "Wildflower" exhibit intent ambiguity, simultaneously denoting a restaurant chain, a retail product, and a floral item. Traditional classifiers force a winner-takes-all assignment, while general-purpose LLMs hallucinate unavailable inventory. We introduce an Agentic Multi-Source Grounded system that addresses both failure modes by grounding LLM inference in (i) a staged catalog entity retrieval pipeline and (ii) an agentic web-search tool invoked autonomously for cold-start queries. Rather than predicting a single label, the model emits an ordered multi-intent set, resolved by a configurable disambiguation layer that applies deterministic business policies and is designed for extensibility to personalization signals. This decoupled design generalizes across domains, allowing any marketplace to supply its own grounding sources and r...