Prototype is the first realization of a scalable, hardware-efficient quantum computing architecture based on bosonic quantum error correction.
In experiments involving real quantum devices and algorithms, automated-reasoning-based method for mapping quantum computations onto quantum circuits is 26 times as fast as predecessors.
Training infrastructure, benchmarks, responsible-AI methodology, and more.
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The Amazon AGI SF Lab will focus on developing new foundational capabilities for enabling useful AI agents.
Customer-obsessed science
Research areas
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February 20, 2025Using large language models to generate training data and updating models through both fine tuning and reinforcement learning improves the success rate of code generation by 39%. -
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December 24, 2024
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Inaugural global university competition focused on advancing secure, trusted AI-assisted software development.
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Led by David Luan and Pieter Abbeel, the lab will focus on developing new foundational capabilities for enabling useful AI agents.
The company's new state-of-the-art foundation models deliver frontier intelligence and industry-leading price performance.
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2025Mitigating the retention of sensitive or private information in large language models is essential for enhancing privacy and safety. Existing unlearning methods, like Gradient Ascent and Negative Preference Optimization, directly tune models to remove unwanted information. However, these methods often become unstable because they fine-tune by maximizing cross-entropy loss, which is the opposite of traditional
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2025Previous text-to-SQL datasets and systems have primarily focused on user questions with clear intentions that can be answered. However, real user questions can often be ambiguous with multiple interpretations or unanswerable due to a lack of relevant data. In this work, we construct a practical conversational text-to-SQL dataset called PRACTIQ, consisting of ambiguous and unanswerable questions inspired
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Query-to-Product Type (Q2PT) is a crucial e-commerce query understanding signal, which directly influences search results relevance and customer UX experience. This imposes high standards on the industrial Q2PT classification models, which have to be regularly monitored for quality among all predicted product types and use cases at scale. Existing solutions for such Q2PT model evaluation involve human-labeled
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2025Task-oriented Dialog systems (ToD) are essential in automating user interactions, but their complex design and dynamic nature make evaluation particularly challenging. Current evaluation methodologies heavily depend on human annotators, which can be inefficient, subjective, and expensive to scale. To advance the field, there is a pressing need for a reliable, scalable, and systematic evaluation framework
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2025Constrained decoding with lookahead heuristics (CDLH) is a highly effective method for aligning LLM generations to human preferences. However, the extensive lookahead rollout operations for each generated token makes CDLH prohibitively expensive, resulting in low adoption in practice. In contrast, common decoding strategies such as greedy decoding are extremely efficient, but achieve very low constraint
Academia
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
The Amazon Nova AI Challenge is a global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
