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.
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.
With a novel parallel-computing architecture, a CAD-to-USD pipeline, and the use of OpenUSD as ground truth, a new simulator can explore hundreds of sensor configurations in the time it takes to test just a few physical setups.
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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Featured news
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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Accurate intent classification is critical for efficient routing in customer service, ensuring customers are connected with the most suitable agents while reducing handling times and operational costs. However, as companies expand their product lines, intent classification faces scalability challenges due to the increasing number of intents and variations in taxonomy across different verticals. In this
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KDD 2025 Workshop on Structured Knowledge for Large Language Models2025Despite advances in large language model (LLM)-based natural language interfaces for databases, scaling to enterprise-level data catalogs remains an under-explored challenge. Prior works addressing this challenge rely on domain-specific fine-tuning—complicating deployment—and fail to leverage important semantic context contained within database metadata. To address these limitations, we introduce a component-based
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Financial accounting systems rely heavily on subledgers to track detailed transaction records. However, modern systems often evolve into complex architectures where different components use inconsistent labeling conventions, making it difficult to understand and utilize important relationships within subledger data. This paper presents a novel framework LLM-STARS (LLM-Enhanced Standardization of Time-series
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KDD 2025 Workshop on Prompt Optimization2025Despite advances in the multilingual capabilities of Large Language Models (LLMs), their performance varies substantially across different languages and tasks. In multilingual retrieval-augmented generation (RAG)-based systems, knowledge bases (KB) are often shared from high-resource languages (such as English) to lowresource ones, resulting in retrieved information from the KB being in a different language
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2025We introduce Griffin, the first foundation model attemptation designed specifically for Relational Databases (RDBs). Unlike previous smaller models focused on single RDB tasks, Griffin unifies the data encoder and task decoder to handle diverse tasks. Additionally, we enhance the architecture by incorporating a cross-attention module and a novel aggregator. Griffin utilizes pretraining on both single-table
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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.
