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.
How agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
New technologies are helping vulnerable communities produce maps that integrate topographical, infrastructural, seasonal, and real-time data — an essential tool for many humanitarian endeavors.
In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.
"Kaputt" features 238,000+ images that advance the state of the art in detecting visual defects across complex retail applications.
Customer-obsessed science
Research areas
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September 26, 20259 min readTo transform scientific domains, foundation models will require physical-constraint satisfaction, uncertainty quantification, and specialized forecasting techniques that overcome data scarcity while maintaining scientific rigor. -
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September 2, 20253 min read
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August 21, 20257 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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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
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IJCNLP-AACL 20252025Dense Retrieval (DR) models have proven to be effective for Document Retrieval and Information Grounding tasks. Usually, these models are trained and optimized for improving the relevance of top-ranked documents for a given query. Previous work has shown that popular DR models are sensitive to the query and document lexicon: small variations of it may lead to a significant difference in the set of retrieved
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.
