In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.
The submission period closes on November 12, 2025
New technologies are helping vulnerable communities produce maps that integrate topographical, infrastructural, seasonal, and real-time data — an essential tool for many humanitarian endeavors.
How agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
"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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November 6, 2025A 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. -
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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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The predominant approach for training web navigation agents gathers human demonstrations for a set of popular websites and hand-written tasks, but it is becoming clear that human data is an inefficient resource. We develop a pipeline to facilitate internet-scale training for agents without laborious human annotations. In the first stage, an LLM generates tasks for 150k diverse websites. In the next stage
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2025Effectively selecting data from population subgroups where a model performs poorly is crucial for improving its performance. Traditional methods for identifying these subgroups often rely on sensitive information, raising privacy issues. Additionally, gathering such information at runtime might be impractical. This paper introduces a cost-effective strategy that addresses these concerns. We identify underperforming
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2025Goal-oriented script planning, or the ability to plan coherent sequences of actions toward specific goals, is commonly used by humans to plan for daily activities. In e-commerce, customers increasingly seek LLM-based assistants to plan for them with a script and recommend products at each step, thereby facilitating convenient and efficient shopping experiences. However, this capability remains under-explored
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2025A generalist foundation model agent needs to have a large and diverse skill repertoire, such as finding directions between two travel locations and buying specific items from the Internet. If each skill needs to be specified manually through a fixed set of human-annotated instructions, the agent’s skill repertoire will necessarily be limited due to the scalability of human-annotated instructions. In this
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2025Given a semi-structured knowledge base (SKB), where text documents are interconnected by relations, how can we effectively retrieve relevant information to answer user questions? Retrieval-Augmented Generation (RAG) retrieves documents to assist large language models (LLMs) in question answering; while Graph RAG (GRAG) uses structured knowledge bases as its knowledge source. However, many questions require
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.
