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

• URL: http://arxiv.org/abs/2603.01486v1
• Date: Mon, 02 Mar 2026 05:51:05 GMT
• Title: Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study
• Abstract summary: We introduce an Agentic Multi-Source Grounded system that addresses both failure modes.<n>Rather than predicting a single label, the model emits an ordered multi-intent set.<n>The system is deployed in production, serving over 95% of daily search impressions.
• Score: 0.8924669503280332
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• Autoresearch for Marketplace Catalogs: From Legacy Forms to AI-Native Matching [9.919270135258108]
  Two-sided service marketplaces are moving from deterministic request-form intake to AI-native probabilistic matching.<n> Relying on inferred intent rather than fixed-form fields forces these platforms to regenerate the provider-side preference taxonomy.<n>We present an autoresearch loop that generates this taxonomy, one occupation at a time, and has been deployed in production at a major U.S. consumer services marketplace since April 2026, spanning 132 occupations.
  arXiv  Detail & Related papers  (2026-08-31T19:18:07Z)
• ComplianceGate: Classifier-Gated Multi-Tier LLM Routing for Inference in Regulated Industries [0.609170287691728]
  PII in user queries can reach model endpoints before the system determines whether that data should leave its jurisdictional boundary.<n>We propose a classifier-gated routing architecture that enforces compliance by design.<n>Simple queries reach small, fast models at a fraction of the cost.
  arXiv  Detail & Related papers  (2026-06-30T05:49:50Z)
• A Unified Structured Query Understanding Framework for Industrial Semantic Search [9.913119853395205]
  Query understanding in large-scale industrial search systems is typically implemented as a cascade of disparate, task-specific components.<n>We propose and deploy a unified structured query understanding system that consolidates these heterogeneous functions into a single Small Language Model.<n>We validate this approach through extensive offline and online tests within LinkedIn's Job Search system.
  arXiv  Detail & Related papers  (2026-05-22T19:35:15Z)
• One Model Is Enough: Native Retrieval Embeddings from LLM Agent Hidden States [5.647839536820347]
  This paper introduces a lightweight projection head that maps hidden states directly into the embedding space, eliminating the need for a separate embedding model.<n> Experiments on the QReCC conversational search benchmark show competitive Recall@10 and MRR@10 compared to the standard generate-then-encode pipeline.
  arXiv  Detail & Related papers  (2026-03-09T14:25:35Z)
• Rethinking Deep Research from the Perspective of Web Content Distribution Matching [86.90255285277256]
  We propose WeDas, a Web Content Distribution Aware framework that incorporates search-space structural characteristics into the agent's observation space.<n>We introduce a few-shot probing mechanism that iteratively estimates the Query-Result Alignment Score.<n>As a plug-and-play module, WeDas consistently improves sub-goal completion and accuracy across four benchmarks.
  arXiv  Detail & Related papers  (2026-03-07T14:51:17Z)
• LLM-based Semantic Search for Conversational Queries in E-commerce [1.3645712130536118]
  We present an LLM-based semantic search framework that captures user intent from conversational queries.<n>Our framework achieves strong precision and recall across various settings compared to baseline approaches on a real-world dataset.
  arXiv  Detail & Related papers  (2026-01-23T06:35:28Z)
• OFA-MAS: One-for-All Multi-Agent System Topology Design based on Mixture-of-Experts Graph Generative Models [57.94189874119267]
  Multi-Agent Systems (MAS) offer a powerful paradigm for solving complex problems.<n>Current graph learning-based design methodologies often adhere to a "one-for-one" paradigm.<n>We propose OFA-TAD, a one-for-all framework that generates adaptive collaboration graphs for any task described in natural language.
  arXiv  Detail & Related papers  (2026-01-19T12:23:44Z)
• A Chain-of-Thought Approach to Semantic Query Categorization in e-Commerce Taxonomies [1.1957890510931164]
  Chain-of-Thought (CoT) paradigm combines simple tree-search with semantic scoring.<n>We show how the CoT approach can detect problems within a hierarchical taxonomy.
  arXiv  Detail & Related papers  (2026-01-01T23:36:13Z)
• Connecting the Dots: Training-Free Visual Grounding via Agentic Reasoning [63.109585527799005]
  GroundingAgent is a visual grounding framework that operates without task-specific fine-tuning.<n>It achieves an average zero-shot grounding accuracy of 65.1 % on widely-used benchmarks.<n>It also offers strong interpretability, transparently illustrating each reasoning step.
  arXiv  Detail & Related papers  (2025-11-24T03:11:08Z)
• Lookahead Routing for Large Language Models [24.082620717301477]
  Lookahead is a routing framework that "foresees" potential model outputs and uses these predictions to guide model selection.<n> Empirical evaluations across seven public benchmarks show that Lookahead consistently outperforms existing routing baselines.
  arXiv  Detail & Related papers  (2025-10-22T12:00:21Z)
• LLM-guided Hierarchical Retrieval [54.73080745446999]
  LATTICE is a hierarchical retrieval framework that enables an LLM to reason over and navigate large corpora with logarithmic search complexity.<n>A central challenge in such LLM-guided search is that the model's relevance judgments are noisy, context-dependent, and unaware of the hierarchy.<n>Our framework achieves state-of-the-art zero-shot performance on the reasoning-intensive BRIGHT benchmark.
  arXiv  Detail & Related papers  (2025-10-15T07:05:17Z)
• Heterogeneous LLM Methods for Ontology Learning (Few-Shot Prompting, Ensemble Typing, and Attention-Based Taxonomies) [46.54026795022501]
  We present a comprehensive system for addressing Tasks A, B, and C of the LLMs4OL 2025 challenge.<n>Our approach combines retrieval-augmented prompting, zero-shot classification, and attention-based graph modeling.<n>These modular, task-specific solutions enabled us to achieve top-ranking results in the official leaderboard.
  arXiv  Detail & Related papers  (2025-08-26T20:50:16Z)
• CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
  We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
  arXiv  Detail & Related papers  (2025-08-05T17:55:24Z)
• DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router [57.28685457991806]
  DeepSieve is an agentic RAG framework that incorporates information sieving via LLM-as-a-knowledge-router.<n>Our design emphasizes modularity, transparency, and adaptability, leveraging recent advances in agentic system design.
  arXiv  Detail & Related papers  (2025-07-29T17:55:23Z)
• A Semi-supervised Scalable Unified Framework for E-commerce Query Classification [13.695419069287482]
  E-commerce queries are usually short and lack context, and the information between labels cannot be used.<n>Most existing industrial query classification methods rely on users' posterior click behavior to construct training samples, resulting in a Matthew vicious cycle.<n>We propose a novel Semi-supervised Scalable Unified Framework (SSUF), containing multiple enhanced modules to unify the query classification tasks.
  arXiv  Detail & Related papers  (2025-06-26T06:52:33Z)
• MMSearch-R1: Incentivizing LMMs to Search [49.889749277236376]
  We present MMSearch-R1, the first end-to-end reinforcement learning framework that enables on-demand, multi-turn search in real-world Internet environments.<n>Our framework integrates both image and text search tools, allowing the model to reason about when and how to invoke them guided by an outcome-based reward with a search penalty.
  arXiv  Detail & Related papers  (2025-06-25T17:59:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.