Customer-obsessed science


Research areas
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July 18, 2025Novel graph-based, adversarial, agentic method for generating training examples helps identify — and mitigate — "overrefusal".
Featured news
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2025Automatic speech recognition (ASR) systems can benefit from incorporating contextual information to improve recognition accuracy, especially for uncommon words or phrases. Current approaches like custom vocabularies or prompting with previous transcript segments provide limited contextual control. Compared to existing context biasing methods, RAG promises more flexible and scalable contextual control by
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Web and mobile systems show constant distribution shifts due to the evolvement of services, users, and threats, severely degrading the performance of threat detection models trained on prior distributions. Fast model adaptation with minimal new data is essential for maintaining reliable security measures. A key challenge in this context is the lack of ground truth, which undermines the ability of existing
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2025A small subset of dimensions within language Transformers’ representation spaces emerge as "outliers" during pretraining, encoding critical knowledge sparsely. We extend previous findings on emergent outliers to Encoder-Decoder Transformers and instruction-finetuned models, and tackle the problem of distilling a student Transformer from a larger teacher Trans-former. Knowledge distillation reduces model
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2025At Amazon, we look to our leadership principles every day to guide our decision-making. Our approach to AI development naturally follows from our leadership principle “Success and Scale Bring Broad Responsibility.” As we continue to scale the capabilities of Amazon’s frontier models and democratize access to the benefits of AI, we also take responsibility for mitigating the risks of our technology. Consistent
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2025Recent advancements in code completion models have primarily focused on local file contexts (Ding et al., 2023b; Jimenez et al., 2024). However, these studies do not fully capture the complexity of real-world software development, which often requires the use of rapidlyevolving public libraries. To fill the gap, we introduce LIBEVOLUTIONEVAL, a detailed study requiring an understanding of library evolution
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