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ARXIV:2603.01486 · AI IN E-COMMERCE · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE
ARXIV:2603.01486AI IN E-COMMERCESUBMITTED 19 MAR · 21:31 UTCFRESHNESS STALEarXiv
A novel AI system for accurately understanding customer intent in multi-category marketplaces, boosting search accuracy by over 13%.
Opportunity summary
Pain A novel AI system for accurately understanding customer intent in multi-category marketplaces, boosting search accuracy by over 13%.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence failed
A novel AI system for accurately understanding customer intent in multi-category marketplaces, boosting search accuracy by over 13%. Traditional classifiers force a winner-takes-all assignment, while general-purpose LLMs hallucinate unavailable inventory.
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…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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.
AI in e-commerce moved forward this cycle; last verified April 2026. Public score 8.0/10.
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Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A novel AI system for accurately understanding customer intent in multi-category marketplaces, boosting search accuracy by over 13%.
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Paper Pack
10.48550/arXiv.2603.01486A novel AI system for accurately understanding customer intent in multi-category marketplaces, boosting search accuracy by over 13%.
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.
Source availability
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
failed0 refs; 0 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
A novel AI system for accurately understanding customer intent in multi-category marketplaces, boosting search accuracy by over 13%. Traditional classifiers force a winner-takes-all assignment, while general-purpose LLMs hallucinate unavailable inventory.
METHOD
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...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. 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.
WHY NOW
AI in e-commerce moved forward this cycle; last verified April 2026. Public score 8.0/10.
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.
This is a core claim stated directly in the abstract describing the system's approach.
partial
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 describes a key feature of the system's output and resolution mechanism, explicitly mentioned in the abstract.
partial
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.
This is a specific, quantifiable result directly stated in the abstract and analysis.
partial
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).
This is a specific, quantifiable result from an ablation study, directly stated in the abstract.
partial
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.
This indicates successful deployment and scale, a significant result mentioned in the abstract.
partial
The current implementation is based on offline batch processing, limiting real-time capabilities.
This is a clear limitation explicitly stated in the analysis section.
partial
This decoupled design generalizes across domains, allowing any marketplace to supply its own grounding sources and resolution rules without modifying the core architecture.
This highlights the system's extensibility and broad applicability, a key design feature mentioned in the abstract.
partial
E-commerce platforms across food, retail, and other sectors have a significant interest in improving search relevance, highlighting a clear pain point and making them potential customers.
This statement from the 'product_opportunity' section indicates a market need and potential customer base.
partial
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Concepts
Methods
Materials
Markets
Competitors
A novel AI system for accurately understanding customer intent in multi-category marketplaces, boosting search accuracy by over 13%.
Segment
AI in e-commerce
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Foundation
Extension
Commercially relevant
Conflicting
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 0 sources / 33% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 33% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.