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.
Customer-obsessed science
Research areas
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October 23, 20266 min read“Controllable text to speech” makes speech more expressive, character voices more distinct and consistent. -
October 22, 20266 min read -
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 4, 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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2026Budget delivery in programmatic advertising requires controlling spend across advertising campaigns whose plant gains vary significantly across campaigns and evolve over time, rendering fixed-gain controllers inadequate. This paper proposes an adaptive spend control architecture comprising four components: an input reparameterization that transforms the nonlinear spend-to-control relationship into a near-linear
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2026A neural network often settles on its answer long before it shows it. We make that precise with a training-free tool, the sign lens. It reads a concept straight from a hidden state using only the sign of each coordinate, positive or negative (Nalagatla, 2026): a concept is a set of coordinates that share a fixed sign pattern, detected by counting agreements. No probe, no dictionary, no training. With this
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2026Contrastive fine-tuning of a single embedding model across heterogeneous retrieval tasks is challenging when the tasks differ in label structure. In product-compliance retrieval, some tasks have multi-label supervision — every item carries a set of compliance categories, and many in-batch items share at least one category with the query, producing frequent in-batch false negatives. Other tasks (e.g., cross-catalog
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2026Enterprise analytics agents solve long-horizon tool-use problems over distributed business data, requiring retrieval, reasoning, API calls, code execution, and adaptation to intermediate observations. Supervised fine-tuning (SFT) calibrates tool syntax and teacher-supported behavior, whereas reinforcement learning (RL) can explore reward-supported behaviors beyond demonstrations; applied uniformly, however
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ICML 2026 Workshop on Agents in the Wild2026Pre-deployment testing of conversational agents requires simulating diverse user behaviors, yet real interaction data is scarce for new products and scripted test cases miss critical edge cases. Existing LLM-based user simulators generate personas from demographic templates or fictional backstories, lacking grounding in operational data, validated behavioral controllability, and systematic coverage of agent
Collaborations
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.
