Amazon scientists and policy experts discuss how the company’s responsible-AI pipeline embeds safety and values throughout the AI development lifecycle.
Agentic mechanism enables Amazon and vendors to optimize supply chain management without disclosing private information.
How we reproduced three attacks that extract private training data from AI models and the cryptographic defenses that stop them.
Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty — and keep them adapting without missing a beat.
Isabelle/HOL's balance of expressiveness, automation, and scalability enabled the world's first formally verified cloud hypervisor.
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May 15, 20265 min readA new scaling law that relates particular architectural choices to loss helps identify models that improve throughput by up to 47% with no loss of accuracy. -
May 14, 202616 min read -
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April 15, 20268 min read
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Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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CVPR 2026 EarthVision Workshop2026Building outline extraction from remote sensing imagery traditionally relies on segmentation or detection followed by post-processing to derive polygonal geometries. Despite advances in sequential prediction methods [2, 20], end-to-end extraction remains challenging, often missing buildings or requiring additional refinement steps. In this work, we reformulate building outline extraction as next-coordinate
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ICLR 2026 Workshop on AI with Recursive Self-Improvement2026Foundation-model upgrades frequently break deployed prompt-based systems: target models differ in chat-template conventions, multimodal interfaces, context limits, and structured-output reliability. We study cross-model prompt adaptation: given a prompt program validated on a source model, produce a target-model prompt that preserves a semantic contract and an interface contract under bounded regression
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2026We present a systematic method for pruning edges from causal graphs by leveraging tiered knowledge. We characterize conditions under which edges can be removed from a causal graph while preserving the identifiability of (conditional) causal effects. This result enables causal identification on simplified graphs that are substantially smaller than the original graphs. The approach is particularly valuable
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2026Gradient orthogonalization is a simple strategy that shows great utility in speeding up gradient descent. The Muon optimizer (Jordan et al., 2024b) combines gradient orthogonalization with first-order momentum and achieves significant improvement in data efficiency over Adam/AdamW (Loshchilov & Hutter, 2019a) for language model training. However, when using model parallelism, gradient orthogonalization
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2026Scaling the number of parameters and the size of training data has proven to be an effective strategy for improving large language model (LLM) performance. Yet, as these models grow increasingly powerful and widely deployed, the cost of inference has become a pressing concern. Despite its importance, the tradeoff between model accuracy and inference efficiency remains underexplored. In this work, we examine
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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.
