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.
Translating from natural to structured language, defining truth, and definitive reasoning remain topics of central concern in automated reasoning, but Amazon Web Services’ new Automated Reasoning checks help address all of them.
The language AI agents might speak, sharing context without compromising privacy, modeling agentic negotiations, and understanding users’ commonsense policies are some of the open scientific questions that researchers in agentic AI will need to grapple with.
A new dataset with over 238,000 images challenges and advances the state of the art in visual defect detection for complex retail applications.
From reimagining storage to serverless computing, Aurora continues to push the boundaries of what's possible in database technology.
Customer-obsessed science
Research areas
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September 2, 2025Audible's ML algorithms connect users directly to relevant titles, reducing the number of purchase steps for millions of daily users. -
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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
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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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ACM SIGSPATIAL 20252025Mapping addresses to geolocations accurately is a challenging and important problem, with many real-world applications such as delivery logistics, map building and path finding. High quality embedding of geospatial data (e.g., addresses, geocodes) which is grounded in real world play an important role in success of modeling tasks such as geocoding and address resolution/matching. Existing state-of-the-art
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ISTFA 20252025As integrated circuits continue to scale down to ever-smaller dimensions and increased complexity, traditional failure isolation & analysis (FA/FI), and sample preparation techniques face significant limitations. The increasing density of modern semiconductor devices, now advancing beyond 3 nm technology nodes, presents unprecedented challenges in precise layer-by-layer analysis. Over the past decade, Xenon
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2025The Multimodal Large Language Models (MLLMs) are continually pre-trained on a mixture of image-text caption data and interleaved document data, while the high-quality data filtering towards image-text interleaved document data is under-explored. We propose to train an efficient MLLM as a Unified Mulitmodal Data Quality Classifier to Filter both high-quality image-text caption and interleaved data (UniFilter
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CIKM 20252025Current methods for evaluating large language models (LLMs) typically focus on high-level tasks such as text generation, without targeting a particular AI application. This approach is not sufficient for evaluating LLMs for Responsible AI dimensions like fairness, since protected attributes that are highly relevant in one application may be less relevant in another. In this work, we construct a dataset
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IEEE CDC 20252025Online advertising is typically implemented via real-time bidding, and advertising campaigns are then defined as extremely high-dimensional optimization problems. To solve these problems in light of large scale and significant uncertainties, the optimization problems are modularized in a manner that makes feedback control a critical component of the solution. The control problem, however, is challenging
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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.
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
