Using ensembles of agents to generate and refine interactions annotated with chains of thought improves performance on a battery of benchmarks by an average of 29%.
Language models inspired by specialized processing regions in the brain offer significant time and cost savings.
An ensemble of models, weighted according to their reluctance to flag anomalies, outperforms its predecessors.
Unique end-of-arm tools with three-dimensional force sensors and innovative control algorithms enable robotic arms to “pick” items from and “stow” items in fabric storage pods.
We present Amazon Nova Premier, our most capable multimodal foundation model and teacher for model distillation.
Customer-obsessed science
Research areas
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August 4, 2025Translating 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.
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Created for students keen to build their career in machine learning, the fifth edition of the program is now open for all eligible students from recognized institutes in India.
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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ACL Findings 20252025Large language models (LLMs) have advanced conversational AI assistants. However, systematically evaluating how well these assistants apply personalization—adapting to individual user preferences while completing tasks—remains challenging. Existing personalization benchmarks focus on chit-chat, nonconversational tasks, or narrow domains, failing to capture the complexities of personalized task-oriented
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Modern logistics networks face a critical challenge in performance documentation that consumes substantial resources yet suffers from inconsistent quality, limited expert review, and context-specificity. We present Shifu, an adaptive knowledge acquisition system for automated root cause analysis that learns continuously from operational feedback without requiring gold standard examples. Shifu integrates
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As Generative AI (Gen-AI) continues to evolve rapidly, its potential to transform supply chain operations remains largely unexplored. Narrowing in on retail supply chain, this paper presents a taxonomy diagram that categorizes trends in Gen-AI adoption across various functions thereby mapping current Gen-AI capabilities and identifying immediate opportunities and potential challenges. We identify several
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Supply chain operations generate vast amounts of operational data; however, critical knowledge—such as system usage practices, troubleshooting workflows, and resolution techniques—often remains buried within unstructured communications like support tickets, emails, and chat logs. While Retrieval-Augmented Generation (RAG) systems aim to leverage such communications as a knowledge base, their effectiveness
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Time series forecasting is a critical first step in generating demand plans for supply chains. Experiments on time series models typically focus on demonstrating improvements in forecast accuracy over existing/baseline solutions, quantified according to some accuracy metric. There is no doubt that forecast accuracy is important; however in production systems, demand planners often value consistency and
Academia
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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.
