Runtime optimizations and a thorough evaluation process driven by agentic AI help ensure that models written in PyTorch will execute efficiently and reliably on Trainium systems.
Expanding the mix of synthetic data used to train a tabular foundation model enables it to adapt to new tables without new models and lead the single-model field on the TabArena benchmark.
Using the Neuron Kernel Interface, a Reactor–AWS collaboration tackled the dynamic shapes, memory access patterns, and cache management that make real-time autoregressive diffusion hard—building techniques that generalize across models.
Three new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design.
New research indicates that AI agents learn compressible models of data, which don’t have enough space to enable memorization.
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Research areas
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November 11, 20265 min readStatic analysis of deployed networks helps reduce the risk that sensitive data leaks across partitions, supporting the security and reliability of global-scale networking. -
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October 20, 20263 min read
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The collaboration aims to advance research while broadening participation and translating discovery into real-world solutions.
Submissions open October 1 and close on November 11, 2026
The collaboration will support research in robotics, industrial and systems engineering, and aerospace technology while fostering innovation in foundational and emerging technologies.
The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
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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ICDE 20272027A fundamental challenge of vector search is achieving consistently high recall while minimizing computational costs. Fixed search parameters cause significant performance variance across queries, and conventional evaluation on average recall masks these per-query disparities. We introduce QASP (Query-Adaptive robust vector Search Policy), which predicts the complete recall progression curve per query via
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IAAI 20272027Alexa+ devices with screens display animated icons during conversations to visually reinforce what is being said. The original set of these icons was hand-drawn by designers one at a time, a process that produces polished results but is time-consuming and cannot scale to the range of topics encountered in open-ended conversation. We describe an automated pipeline, deployed in production, that generates
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The American Journal of Managed Care2026Chronic conditions affect 60% of US adults and account for 90% of the US healthcare spending. Advanced primary care (APC) can improve chronic disease management compared to standard primary care. However, evidence on the impact of APCs on clinical outcomes beyond 1 year and in commercially insured populations is limited. The objective of this study is to evaluate the longitudinal association between engagement
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NeurIPS 2026 Workshop on Grounded and Faithful Vision-Language Models for Real-World Deployment (VLM4RWD)2026Vision language action (VLA) policies map images and instructions to robot commands, but their internal representations are difficult to inspect and modify. We build a language interface for a VLA policy whose weights remain fixed. A verbalizer, a separate language model, converts an internal activation into a structured description, and a reconstructor maps the original and edited descriptions back into
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2026Natural language-to-graph query generation (NL2GQL) requires schema-aware reasoning and multi-hop traversal planning, posing graph-specific challenges beyond the extensively studied Text-to-SQL setting. Existing NL2GQL systems typically solve each query in isolation, limiting their ability to learn from prior executions. We present ExGQL, an experience-guided framework for self-improving NL2GQL. ExGQL combines
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View allWhether you're a faculty member or student, there are number of ways you can engage with Amazon.
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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.
