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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November 5, 20266 min readAmazon senior applied scientist and coauthors showed that an overlooked mathematical condition from 1963 proves why frequently used machine-learning optimization methods work well. -
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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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COLM 2026 Workshop on Context Beyond the Window2026Deploying LLMs for enterprise Text-to-SQL is bottlenecked less by the model than by what context reaches it: business logic spans thousands of tables, and no model can ingest a full catalog at once. We argue that the most effective place to intervene is therefore the knowledge-base context the model consumes, and that this context should be constructed from historical usage rather than tuned for as a fixed
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COLM 2026 Workshop on Agent Behavior2026Single-metric task-success scores systematically misrank LLM agents in domain-specific deployments: two configurations can achieve identical end-to-end accuracy while exhibiting structurally different production behaviors — silent argument hallucination, infinite tool loops, redundant retries, semantically-equivalent-but-syntactically-divergent queries. We propose a behavioral evaluation protocol that decomposes
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ACM SIGSPATIAL 20262026Accurate map paths are crucial for routing vehicles, pedestrians, and autonomous systems, yet keeping that data fresh demands continual editing of features and geometry, and labeling of attributes such as surface type and lane count. At scale this is labor-intensive, and each change calls for spatial reasoning and verification that has resisted full automation, keeping a human in the loop. We present MapScout
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ACM SIGSPATIAL 20262026Last-mile logistics efficiency largely depends on map data being accurate, fresh, and having high geospatial coverage. Operators must preserve these qualities when migrating between heterogeneous map data providers. Existing work in the literature addresses road-geometry conflation; however, the conflation of road network attributes and their transition relations remains under-studied, especially at a production-level
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CIKM 20262026Production search indices operate under strict storage and latency budgets that prevent indexing the full corpus (e.g.: web documents). When only a fraction of documents can be retained, the system must decide which ones to keep. Existing pruning methods make this decision using query-agnostic signals, including lexical quality, embedding magnitude, and corpus centrality or single-feature query-aware heuristics
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.
