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.
Customer-obsessed science
Research areas
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October 1, 202610 min readAugmenting a network graph with agentic AI produces a “digital twin” that can help isolate network failures. -
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August 21, 20269 min read
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July 30, 20268 min read
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July 29, 20266 min read
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The collaboration will support research in robotics, industrial and systems engineering, and aerospace technology while fostering innovation in foundational and emerging technologies.
Submissions open October 1 and close on November 4, 2026
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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2026When we fine-tune an LLM on code in a new programming language, what actually changes inside the model? Does it learn new algorithms, new syntax, or does it develop the ability to faithfully implement known algorithms in an unfamiliar language? This question is hard to answer with existing languages because we cannot disentangle what the model learned from fine-tuning versus what it already knew from pretraining
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2026Group relative policy optimization for reinforcement learning with verifiable rewards (RLVR) typically uses a fixed importance-sampling (IS) ratio clipping boundary across all rollouts. We identify a key limitation: rare correct rollouts on harder problems and abundant correct rollouts on easier problems are clipped at comparable rates, despite contributing very different learning signals. Rollouts with
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2026Synthetic dialogue corpora are increasingly used as proxies for target data, yet personagrounded generators optimize individual conversations rather than corpus composition, yielding plausible dialogues with distorted population-level behavior mixes. We introduce GroupPersona, a framework that aligns synthetic corpora with a reference behavior distribution. GroupPersona turns population statistics into
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RecSys 20262026We present a production-scale playlist curation system on Amazon Music that leverages modular architecture and holistic LLM-powered reasoning to generate playlists. Our system leverages LLMs to reason over rich track metadata—including genre, mood, era, sonic descriptions, and artist context—to select cohesive track sets that satisfy both relevance and coherence criteria. We formulate the curation task
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2026Matching users to interest categories at scale is central to personalized shopping, but the task is challenging in large e-commerce platforms, where label spaces continually evolve and user-interest signals are sparse and long tailed. Autoregressive language models are appealing because their world knowledge and semantic priors over descriptors generalize across extreme label spaces and accommodate multiple
Collaborations
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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.
