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June 8, 20267 min readFour approaches can dramatically improve the performance and trustworthiness of AI agents in operational environments. -
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May 26, 20265 min read
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Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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SIGIR 20262026Entity search, i.e., finding the most similar entities to a query entity, faces unique challenges in e-commerce, where product similarity varies across categories and contexts. Traditional embedding-based approaches often struggle to capture nuanced context-specific attribute relevance. In this paper, we present a two-stage approach combining Large Language Model (LLM)-driven attribute graph construction
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SIGIR 20262026Music search at the scale of Amazon Music presents a unique challenge: queries frequently deviate from indexed metadata due to misspellings, transpositions, and phonetic variations, yet the retrieval system must operate under strict millisecond-level latency constraints. Our existing learning-to-retrieve system, the High Confidence Index (HCI), learns query-entity associations from customer behavior, relying
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SIGIR 20262026Selecting which recommendation algorithm variant to advance to online experimentation is a critical decision in industry practice. Manual evaluation is subjective and time-consuming, while offline metrics such as nDCG often fail to correlate with real-world customer preferences. We present SAER, a two-stage framework (pointwise filtering and pairwise comparison) that uses Large Language Models as judges
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SIGIR 20262026Recommending visually compatible products in fashion and interior design is a significant challenge, as compatibility rules are nuanced, context-dependent, and reliant on fine-grained details that traditional models fail to capture. Existing methods often struggle with heterogeneous compatibility rules (e.g., sofa-table vs. sofa-curtain) and an over-reliance on global visual features, missing critical textual
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2026Deep Research (DR) systems autonomously retrieve and synthesize information from web sources, however, industrial DR applications face a critical gap: effective integration of internal tools with web search. In this work, we introduce DeepResearch Retail, an evaluation framework grounded in real-world e-commerce data for assessing Deep Research with tools (DR+Tools) in realistic commercial settings. The
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
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