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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2025Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous agents typically rely on complex prompting or extensive fine-tuning, which often fails to introduce new capabilities while preserving strong generalizability. We introduce Hephaestus-Forge, the first large-scale pre-training corpus designed to enhance the fundamental capabilities of LLM agents in API function calling, intrinsic
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2025Large language models (LLMs) have achieved remarkable success in various natural language generation (NLG) tasks, but their performance in automatic text evaluation is not yet ready as human replacements. In this paper, we propose SEEval (Self-Explanation in Evaluation), a novel prompt-based text evaluator. Inspired by educational psychology, SEEval incorporates self-explanation, a metacognitive strategy
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2025Machine unlearning has been used to remove unwanted knowledge acquired by large language models (LLMs). In this paper, we examine machine unlearning from an optimization perspective, framing it as a regularized multi-task optimization problem, where one task optimizes a forgetting objective and another optimizes the model performance. In particular, we introduce a normalized gradient difference (NGDiff)
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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
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.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
