ReformIR adaptively prioritizes reformulations and documents with a surrogate model guided by ranker feedback to boost recall while suppressing drift under fixed reranking budgets.
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2026 2verdicts
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An agentic multi-source grounding system for marketplace query intent achieves 90.7% accuracy on long-tail queries at DoorDash by combining catalog grounding, web search, and deterministic disambiguation, outperforming baselines by up to 13pp.
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When More Reformulations Hurt: Avoiding Drift using Ranker Feedback
ReformIR adaptively prioritizes reformulations and documents with a surrogate model guided by ranker feedback to boost recall while suppressing drift under fixed reranking budgets.
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Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study
An agentic multi-source grounding system for marketplace query intent achieves 90.7% accuracy on long-tail queries at DoorDash by combining catalog grounding, web search, and deterministic disambiguation, outperforming baselines by up to 13pp.