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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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2025The efficient implementation of large language models (LLMs) is crucial for deployment on resource-constrained devices. Low-rank tensor compression techniques, such as tensor-train (TT) networks, have been widely studied for over-parameterized neural networks. However, their applications to compress pre-trained large language models (LLMs) for downstream tasks (post-training) remains challenging due to
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Transactions on Machine Learning Research2025Pattern discovery in data plays a crucial role across diverse domains, including healthcare, risk assessment, and machinery maintenance. In contrast to black-box deep learning models, symbolic rule discovery emerges as a key data mining task, generating human-interpretable rules that offer both transparency and intuitive explainability. This paper introduces the Optimal Pattern Detection Tree (OPDT), a
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2025Effective product schema modeling is fundamental to e-commerce success, enabling accurate product discovery and superior customer experience. However, traditional manual schema modeling processes are severely bottlenecked, producing only tens of attributes per month, which is insufficient for modern e-commerce platforms managing thousands of product types. This paper introduces AttributeForge, the first
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IEEE CogMI 20252025Operational support is an important component of production software services. Support requests are often emergent and can come in many forms such as customer escalations or unplanned service interruption. Engineers across organizations have been successful in implementing automation to help streamline support processes but many solutions remain in the hands of human operators. A successful strategy to
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Winter Simulation Conference 20252025Simulation plays a central role in the strategic planning and operational evaluation of supply chain networks. Within these networks, order fulfillment traditionally requires solving computationally expensive optimization problems in real-time across multiple constraints. For forward-looking simulations evaluating millions of orders, such optimization becomes prohibitively expensive. We develop a neural
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