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

arXiv 2603.01486 ↗First posted 2 Mar 2026cs.AIcited by 2

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

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 resolution rules without modifying the core architecture. Evaluated on DoorDash's multi-vertical search platform, the system achieves +10.9pp over the ungrounded LLM baseline and +4.6pp over the legacy production system. On long-tail queries, incremental ablations attribute +8.3pp to catalog grounding, +3.2pp to agentic web search grounding, and +1.5pp to dual intent disambiguation, yielding 90.7% accuracy (+13.0pp over baseline). The system is deployed in production, serving over 95% of daily search impressions, and establishes a generalizable paradigm for applications requiring foundation models grounded in proprietary context and real-time web knowledge to resolve ambiguous, context-sparse decision problems at scale.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 12 of its 27 references that are in the map. First posted on arXiv 2 Mar 2026.

Builds on

12 of 27 references.

References · 12

2020Language Models are Few-Shot Learnerscited by 32,419
2015Distilling the Knowledge in a Neural Networkcited by 10,324
2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
2022Survey of Hallucination in Natural Language Generationcited by 2,075
2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
2023Large Language Models for Information Retrieval: A Surveycited by 331
2024Sufficient Context: A New Lens on Retrieval Augmented Generation Systemscited by 72
2022QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillationcited by 21
2025REIC: RAG-Enhanced Intent Classification at Scalecited by 3
2025A Semi-supervised Scalable Unified Framework for E-commerce Query Classificationcited by 2
2024Concept Distillation from Strong to Weak Models via Hypotheses-to-Theories Promptingcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

2026Multi-LLM Query Optimizationcited by 1
2026Decoupling Search from Reasoning: A Vendor-Agnostic Grounding Architecture for LLM Agentscited by 1

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