A family of four foundation models designed to meet diverse enterprise needs across reasoning, multimodal processing, and real-time conversational AI.
How agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
Reasoning models can generate seven to 10 times as many tokens as necessary on simple tasks, creating unsustainable costs at scale. Amazon's vision for metacognitive AI could fundamentally shift how models allocate computational resources.
Amazon's competitive-agent architecture creates a continuous improvement cycle that develops security protections at machine speed, reducing what typically takes weeks down to hours.
A new approach to reducing carbon emissions reveals previously hidden emission “hotspots” within value chains, helping organizations make more detailed and dynamic decisions about their future carbon footprints.
Customer-obsessed science
Research areas
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December 5, 20256 min readA multiagent architecture separates data perception, tool knowledge, execution history, and code generation, enabling ML automation that works with messy, real-world inputs. -
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November 20, 20254 min read
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October 20, 20254 min read
Featured news
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
The collaboration will advance research in generative AI, robotics, natural language processing and cloud computing while fostering innovation in foundational and emerging technologies.
University teams battle to harden and hack AI coding assistants in head-to-head tournament
David Chang/Getty Images/iStockphoto
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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QCE 20252025The rapid evolution of quantum hardware necessitates an adaptable static analysis framework for validating quantum programs. In this work, we introduce SHARP, a rule-based static analysis framework designed for OpenQASM that decouples hardware-specific constraints from the validation engine. By employing a rule-based approach, SHARP allows quantum computing services to validate programs against evolving
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Code@MIT 20252025User-randomized A/B testing, while the gold standard for online experimentation, faces significant limitations when legal, ethical, or practical considerations prevent its use. Item-level randomization offers an alternative but typically suffers from high variance and low statistical power due to skewed distributions and limited sample sizes. We here introduce Regular Balanced Switchback Designs (RBSDs)
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Code@MIT 20252025This paper examines the effectiveness of stratification in experimental design using evidence from multiple large-scale experiments. We analyze data from experiments ranging from approximately 30,000 to 180,000 units across different business contexts. Our results show that pre-stratification and post-stratification achieve virtually identical precision improvements - largest in smaller samples (10% improvement
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Code@MIT 20252025Determining appropriate experimental duration remains a challenging problem in online experimentation. While experimenters ideally would know in advance how long to run experiments in order to inform confident business decisions, many factors affecting conclusiveness of their results are difficult to predict prior to the experiment. Consequently, experimentation services develop 'in-flight' tools that suggest
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NeurIPS 2025 Workshop on Efficient Reasoning2025Large reasoning models (LRMs) excel at reasoning tasks but face deployment barriers due to computational constraints, regulatory requirements, and domain-specific knowledge gaps. This work addresses these limitations by developing cost-efficient post-training methods to enhance reasoning capabilities. Using Qwen3-4B as our base model, we investigate variations of efficient Supervised Fine-Tuning (SFT) and
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
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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.
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.
