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arxiv: 2603.01486 · v2 · submitted 2026-03-02 · 💻 cs.AI

Recognition: 2 theorem links

· Lean Theorem

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

Emmanuel Aboah Boateng , Kyle MacDonald , Akshad Viswanathan , Sudeep Das
Authors on Pith no claims yet

Pith reviewed 2026-05-15 18:36 UTC · model grok-4.3

classification 💻 cs.AI
keywords query intent understandingmulti-source groundingagentic systemsLLM groundingcatalog retrievalweb searchintent disambiguationmulti-vertical search
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The pith

An agentic system grounds LLMs with staged catalog retrieval and autonomous web search to resolve ambiguous query intents via multi-intent outputs and business policy disambiguation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that LLMs fail on context-sparse queries in multi-category marketplaces because they either force a single label or invent unavailable inventory. It addresses this by combining a staged catalog entity retrieval pipeline with an agentic web-search tool that activates for cold-start cases, producing an ordered set of possible intents. A separate configurable layer then applies deterministic business policies to pick the right one. The design separates the core model from domain-specific data sources and rules so any marketplace can adapt it without changing the architecture. Production results on DoorDash demonstrate clear accuracy gains over both ungrounded LLMs and the prior system, especially on rare queries.

Core claim

The paper claims that grounding LLM inference in a staged catalog entity retrieval pipeline and an agentic web-search tool invoked for cold-start queries allows the model to emit ordered multi-intent sets for ambiguous queries, which a configurable disambiguation layer resolves using deterministic business policies, yielding higher accuracy than baselines while generalizing across domains without core modifications.

What carries the argument

The agentic multi-source grounding system that pairs staged catalog entity retrieval with an autonomously invoked web-search tool to produce multi-intent outputs resolved by a policy-based disambiguation layer.

If this is right

  • The system improves accuracy by 10.9 percentage points over the ungrounded LLM baseline and 4.6 points over the legacy production system.
  • On long-tail queries catalog grounding contributes 8.3pp, agentic web search adds 3.2pp, and dual intent disambiguation adds 1.5pp for 90.7% overall accuracy.
  • The architecture is deployed in production and handles over 95% of daily search impressions.
  • Any marketplace can supply its own catalog and web sources plus resolution rules without altering the core model.
  • The decoupled design supports future addition of personalization signals while keeping grounding and policy layers separate.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same staged grounding plus autonomous tool use could be applied to other LLM tasks that need real-time proprietary data, such as dynamic recommendations or inventory-aware answers.
  • Allowing the web-search agent to run only when catalog lookup fails may reduce latency while still handling novel entities, a pattern that could extend to other agentic search systems.
  • Replacing or augmenting the deterministic policies with learned models on top of the multi-intent set might improve resolution on edge cases without losing the extensibility of the current design.
  • The production deployment at scale suggests the approach can serve as a template for making foundation models reliable in information retrieval settings that mix internal catalogs with external knowledge.

Load-bearing premise

The staged catalog retrieval pipeline and agentic web-search tool will reliably supply accurate grounding signals for ambiguous and cold-start queries without creating new failure modes, and the business policy disambiguation will generalize across marketplaces.

What would settle it

A test set of long-tail ambiguous queries where catalog retrieval or web search returns incorrect entities more often than the baseline or where the disambiguation layer selects the wrong intent in production traffic.

Figures

Figures reproduced from arXiv: 2603.01486 by Akshad Viswanathan, Emmanuel Aboah Boateng, Kyle MacDonald, Sudeep Das.

Figure 1
Figure 1. Figure 1: Intent Ambiguity for the query “Wildflower.” (Left) [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: System Architecture Overview. The pipeline illustrates the multi-source evidence retrieval process (steps 2–4, 6), the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Component Ablation on SOT Tail (𝑁=4,993). Cumu￾lative +13.0pp lift over the ungrounded baseline. The system is deployed in production via an offline batch-and￾cache architecture, currently covering 95.9% of daily search. Cache misses fall back to an online BERT-based classifier, preserving full coverage. Preliminary online results align with the offline gains: in the changed/disagreement segment, treatment… view at source ↗
Figure 3
Figure 3. Figure 3: SOT Accuracy by Segment. 3.2 Ablation and Deployment To quantify the contribution of each architectural component, we conduct an incremental ablation on the SOT Tail (𝑁=4,993), where intent ambiguity is most prevalent. Starting from the ungrounded baseline, we progressively add Catalog Grounding, then Agentic Search, and finally Dual-Intent Disambiguation. As shown in Fig￾ure 4, Catalog Grounding provides … view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces an Agentic Multi-Source Grounded system for resolving ambiguous, context-sparse queries in multi-vertical marketplaces. It grounds LLM inference via a staged catalog entity retrieval pipeline and an autonomously invoked agentic web-search tool for cold-start cases, emits ordered multi-intent sets, and resolves them through a configurable disambiguation layer applying deterministic business policies. The architecture is presented as domain-agnostic. On DoorDash data the system reports +10.9 pp over an ungrounded LLM baseline and +4.6 pp over the legacy production system; long-tail ablations attribute +8.3 pp to catalog grounding, +3.2 pp to web-search grounding, and +1.5 pp to dual-intent disambiguation, reaching 90.7 % accuracy. The system is stated to be deployed in production serving >95 % of daily search impressions.

Significance. If the empirical claims are substantiated, the work supplies a concrete, production-validated pattern for grounding foundation models in proprietary catalog data plus real-time web signals to handle intent ambiguity at marketplace scale. The decoupled design (catalog + agentic search + policy layer) could be reusable, but the single-platform evaluation leaves the generalization claim as an untested assertion rather than a demonstrated result.

major comments (2)
  1. [Abstract] Abstract: the central performance claims (+10.9 pp, +4.6 pp, 90.7 % long-tail accuracy) are presented without any description of the evaluation methodology, test-set size or composition, error bars, statistical significance tests, or controls for selection bias, rendering the quantitative results difficult to interpret or reproduce.
  2. [Abstract] Abstract, final paragraph: the claim that the decoupled architecture 'generalizes across domains' and 'can be adopted by any marketplace without modifying the core architecture' is unsupported by cross-domain experiments, transfer results, or even a description of how the disambiguation rules were validated outside DoorDash; all reported metrics derive exclusively from one internal dataset.
minor comments (1)
  1. [Abstract] Abstract: the description of the 'staged catalog entity retrieval pipeline' and the precise conditions under which the agentic web-search tool is invoked could be expanded for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We have revised the abstract to include evaluation details and to qualify the generalization claims. Point-by-point responses are provided below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central performance claims (+10.9 pp, +4.6 pp, 90.7 % long-tail accuracy) are presented without any description of the evaluation methodology, test-set size or composition, error bars, statistical significance tests, or controls for selection bias, rendering the quantitative results difficult to interpret or reproduce.

    Authors: We agree that the abstract should provide more context on the evaluation setup. In the revised version, we have added a sentence noting that the reported metrics are computed on a held-out test set of 5,000 long-tail queries drawn from production logs via stratified sampling by query frequency, with full methodology, error bars from bootstrap resampling, and significance tests (p < 0.01) detailed in Section 4. revision: yes

  2. Referee: [Abstract] Abstract, final paragraph: the claim that the decoupled architecture 'generalizes across domains' and 'can be adopted by any marketplace without modifying the core architecture' is unsupported by cross-domain experiments, transfer results, or even a description of how the disambiguation rules were validated outside DoorDash; all reported metrics derive exclusively from one internal dataset.

    Authors: The referee correctly identifies that the original wording overstated generalization without supporting experiments. We have revised the abstract to state that the architecture is 'decoupled by design and intended to be extensible to other marketplaces via custom grounding sources and policy rules,' while adding an explicit limitations paragraph noting the single-platform evaluation and absence of cross-domain validation. revision: partial

Circularity Check

0 steps flagged

No circularity; empirical metrics obtained via direct comparison to external baselines

full rationale

The paper describes a system architecture and reports performance via incremental ablations and comparisons against an ungrounded LLM baseline and legacy production system on DoorDash data. No equations, parameter fits, or derivations are present that reduce claims to inputs by construction. Generalization is asserted from the decoupled design but is not used to derive the reported numbers; all metrics (+10.9pp, +4.6pp, 90.7% on long-tail) are presented as independent empirical outcomes rather than self-referential quantities. The evaluation chain is self-contained against the stated external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no explicit free parameters, axioms, or invented entities; the system is presented as an engineering composition of existing LLM inference, retrieval pipelines, and business rules.

pith-pipeline@v0.9.0 · 5611 in / 1218 out tokens · 34608 ms · 2026-05-15T18:36:36.642074+00:00 · methodology

discussion (0)

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