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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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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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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2026Points-of-Interest (POI) search, the task of retrieving and ranking geographically situated entities in response to a query issued from a specific location, underpins major mapping and navigation services. We present POI Search Suite (POISS), the first large-scale dataset designed for this task, pairing over 300K geo-anchored natural language queries across 7 languages and 14 locales with more than 4M graded
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2026 IEEE Sensor Data Fusion: Trends, Solutions, Applications2026Transformer architecture has shown great success in many challenging problems such as natural language processing. It excels at discovering features and correlations in large amounts of data. Sensor fusion is another technology that requires this kind of processing power and reasoning capability. Researchers have applied transformer architecture in sensor fusion model technologies. However, most of them
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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.
