Customer-obsessed science


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August 11, 2025Trained on millions of hours of data from Amazon fulfillment centers and sortation centers, Amazon’s new DeepFleet models predict future traffic patterns for fleets of mobile robots.
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Featured news
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The Web Conf 2025 Workshop on Resource-Efficient Learning for the Web2025E-commerce has experienced significant growth recently, generating vast amounts of data on user preferences, interactions, and purchase patterns. Effectively modeling and representing users and products in these online ecosystems is crucial for various applications. However, existing approaches for e-commerce representation learning face several limitations: (i) they primarily consider user behavior patterns
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IJCNN 20252025Latent entity extraction (LEE) tackles the challenge of identifying implicit, contextually inferred entities within free text—an area where traditional entity extraction methods fall short. In this paper, we introduce LentEx, a novel framework for latent entity extraction that leverages synthetic data generation and instruction fine-tuning to optimize smaller, efficient large language models (LLMs). Latent
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ACL Findings 20252025Large language models (LLMs) have advanced conversational AI assistants. However, systematically evaluating how well these assistants apply personalization—adapting to individual user preferences while completing tasks—remains challenging. Existing personalization benchmarks focus on chit-chat, nonconversational tasks, or narrow domains, failing to capture the complexities of personalized task-oriented
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Modern logistics networks face a critical challenge in performance documentation that consumes substantial resources yet suffers from inconsistent quality, limited expert review, and context-specificity. We present Shifu, an adaptive knowledge acquisition system for automated root cause analysis that learns continuously from operational feedback without requiring gold standard examples. Shifu integrates
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As Generative AI (Gen-AI) continues to evolve rapidly, its potential to transform supply chain operations remains largely unexplored. Narrowing in on retail supply chain, this paper presents a taxonomy diagram that categorizes trends in Gen-AI adoption across various functions thereby mapping current Gen-AI capabilities and identifying immediate opportunities and potential challenges. We identify several
Academia
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