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.
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.
How the Verus "program verifier", which automatically checks code against a mathematical specification of its functionality, helps increase security assurance in software projects.
Ten years after we founded the Automated Reasoning Group, mathematical logic has moved from academic research into production services that secure millions of customer workloads — demonstrating that systems can be provably correct, not just probably correct.
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October 29, 20265 min readTwo optimization techniques, when combined, can significantly improve a model’s speed and cost effectiveness without sacrificing quality. -
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October 20, 20263 min read
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October 12, 202614 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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QIP 20202020We prove that quantum Gibbs states of spin systems above a certain threshold temperature are approximate quantum Markov networks, meaning that the conditional mutual information decays rapidly with distance. We prove exponential decay (power-law decay) for short-ranged (long-ranged) interacting systems. As consequences, we establish the efficiency of quantum Gibbs sampling algorithms, a strong version of
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QIP 20202020The concept of quantum complexity has far-reaching implications spanning theoretical computer science, quantum many-body physics, and high energy physics. The quantum complexity of a unitary transformation or quantum state is defined as the size of the shortest quantum computation that executes the unitary or prepares the state. It is reasonable to expect that the complexity of a quantum state governed
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Physical Review Research2020Topological entanglement entropy has been extensively used as an indicator of topologically ordered phases. We study conditions for two-dimensional topologically trivial states to exhibit spurious contributions which suffer topological entanglement entropy. We show that if the state at the boundary of a subregion is a stabilizer state, then it has non-zero spurious contribution on the region if, and only
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Interspeech 20202020Speaker identification based on voice input is a fundamental capability in speech processing enabling versatile downstream applications, such as personalization and authentication. With the advent of deep learning, most state-of-the-art methods apply machine learning techniques and derive acoustic embeddings from utterances with convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
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Interspeech 20202020Entity Linking (EL) recognizes textual mentions of entities and maps them to the corresponding entities in a Knowledge Graph (KG). In this paper, we propose a novel method for EL on short text using entity representations base on their name labels, descriptions, and other related entities in the KG. We then leverage a pre-trained BERT model to calculate the semantic similarity between the entity and the
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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.
