Customer-obsessed science


Research areas
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July 22, 2025Generating diverse synthetic prior distributions leads to a tabular foundation model that outperforms task-specific baselines.
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2025Visual agent models for automating human activities on Graphical User Interfaces (GUIs) have emerged as a promising research direction, driven by advances in large Vision Language Models (VLMs). A critical challenge in GUI automation is the precise grounding of interface elements across diverse platforms. Existing vision-only GUI agents directly ground elements from large and cluttered screenshots, requiring
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2025Language models have led to a leap forward in web automation. The current web automation approaches take the current web state, history actions, and language instruction as inputs to predict the next action, overlooking the importance of history states. However, the highly verbose nature of web page states can result in long input sequences and sparse information, hampering the effective utilization of
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2025Automatic evaluation of retrieval augmented generation (RAG) systems relies on fine grained dimensions like faithfulness and relevance, as judged by expert human annotators. Meta-evaluation benchmarks support the development of automatic evaluators that correlate well with human judgement. However, existing benchmarks predominantly focus on English or use translated data, which fails to capture cultural
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Recent large language models have shown promising capabilities in long-form reasoning, following structured chains of thought before arriving at a final answer. However, we observe that these reasoning paths tend to include substantial redundancy; analyzing attention patterns reveals that attention scores are widely scattered, particularly incorrect answers exhibit greater attention sparsity. In this paper
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Designing intelligent assistants for e-commerce sellers presents significant challenges, primarily due to the abstract nature of seller queries and the complexity of orchestrating multiple internal tools. In-context planning (ICP) has emerged as a promising adaptive problem-solving approach for this setting. However, selecting effective exemplars for ICP remains a difficult problem, largely because of the
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