Customer-obsessed science
Research areas
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August 26, 20265 min readDiscounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives.
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August 21, 20269 min read
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July 30, 20268 min read
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July 9, 202610 min read
Featured news
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ASE 20262026Complex itemized bills from services such as cloud computing, healthcare, or telecommunications can be difficult to understand because the pricing terms that determine what a customer pays are straightforward, but the eligibility rules that determine whether a given line item, a single charge for a product and quantity, qualifies for a given price are scattered across user guides, service terms, and fine
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2026Visual grounding aims to associate free-form textual queries with specific regions in an image. While recent Multimodal Large Language Models (MLLMs) have demonstrated promising capabilities in this domain, they primarily excel at object-level grounding and often struggle with part-level grounding—an essential requirement for fine-grained tasks such as robotic manipulation. In this work, we introduce a
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2026LLM-based agents struggle to execute complex, multi-step Standard Operating Procedures (SOPs) that are fundamental to industrial automation. Existing benchmarks fail to capture the procedural complexity and tool orchestration demands of real-world workflows. We introduce SOP-Bench, a benchmark of 2,000+ tasks from human expert-authored SOPs across 12 business domains (healthcare, logistics, finance, content
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KDD 2026 Workshop on Two-sided Marketplace Optimization2026Optimizing online pricing strategy for Amazon Device dependent products is a uniquely challenging topic in dynamic pricing of two-sided marketplaces when balancing the supply side economics and demand responsiveness. To address this challenge, we propose a scalable framework that integrates a hierarchical segmentation model with a sequential learning layer for high-velocity event (HVE) adjustment, to simulate
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ICTIR 20262026Effectively understanding and modeling the temporal aspects of user queries is crucial for Information Retrieval (IR) and Question Answering (QA), particularly in contexts that demand freshness, historical accuracy, or temporal reasoning. In this paper, we present a formal and comprehensive taxonomy for classifying natural language queries along four dimensions: (i) temporal understanding, (ii) reasoning
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