Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.
New technologies are helping vulnerable communities produce maps that integrate topographical, infrastructural, seasonal, and real-time data — an essential tool for many humanitarian endeavors.
A new dataset with over 238,000 images challenges and advances the state of the art in visual defect detection for complex retail applications.
To transform scientific domains, foundation models will require physical-constraint satisfaction, uncertainty quantification, and specialized forecasting techniques that overcome data scarcity while maintaining scientific rigor.
Customer-obsessed science
Research areas
-
October 16, 2025Amazon vice president and distinguished engineer Marc Brooker explains how agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components. -
Featured news
The collaboration will advance research in generative AI, robotics, natural language processing and cloud computing while fostering innovation in foundational and emerging technologies.
Amazon Research Awards have opened their Fall call for proposals, which closes November 5, 2025.
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.
-
2025Predicting the user’s shopping intent is a crucial task in e-commerce. In particular, determining the product category, which the user wants to shop, is essential for delivering relevant search results and website navigation options. Existing query classification models are reported to have excellent predictive performance on the single-intent queries (e.g. ‘running shoes’), but there is little research
-
2025Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks. However, prompt engineering remains an impediment for end users due to rapid advances in models, tasks, and associated best practices. To mitigate this, Automatic Prompt Optimization (APO) techniques have recently emerged that use
-
2025Recently, textual graph-based retrieval-augmented generation (GraphRAG) has gained popularity for addressing hallucinations in large language models when answering domain-specific questions. Most existing studies assume that generated answers should comprehensively integrate all relevant information from the textual graph. However, this assumption may not always hold when certain information needs to be
-
2025This paper presents a novel approach to building automated knowledge banks for Generative Business Intelligence (GenBI) systems, enabling natural language interfaces to organizational data without specialized engineering expertise. We demonstrate how dashboard definitions can be transformed into knowledge repositories that bridge the semantic gap between Large Language Models (LLMs) and organization-specific
-
RecSys 20252025The user cold-start problem remains a fundamental challenge for sequential recommender systems, particularly in large-scale video streaming services where a substantial portion of users have limited or no historical interaction data. In this work, we formulate an attempt at solving this issue by proposing a framework that leverages Large Language Models (LLMs) to enrich interaction histories using user
Conferences
Collaborations
View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
View all
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.
