A new hybrid optimization approach allows edge devices to fine-tune vision-language models using only forward passes, achieving up to 7% higher accuracy than existing techniques.
"Reinforcement learning gyms" train agents on the many low-level tasks that they must chain together to execute customer requests.
New audio-processing technology is making entertainment more accessible for millions of viewers.
Reasoning models can generate seven to 10 times as many tokens as necessary on simple tasks, creating unsustainable costs at scale. Amazon's vision for metacognitive AI could fundamentally shift how models allocate computational resources.
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January 13, 20267 min readLeveraging existing environment simulators and reward functions based on verifiable ground truth boosts task success rate, even with small models and small training datasets. -
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December 29, 20259 min read
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December 5, 20256 min read
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Hosted by Dr. Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Meet the 63 Amazon Research Award (ARA) recipients, who represent 41 universities in 8 countries.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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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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KDD 2025 Workshop on AI for Supply Chain2025Modern 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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KDD 2025 Workshop on AI for Supply Chain2025As 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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KDD 2025 Workshop on AI for Supply Chain2025Supply 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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KDD 2025 Workshop on AI for Supply Chain2025Time 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
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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.
