Amazon Science
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Imported from official source
Amazon Science — publications from its own official source.
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Publications 29
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Graph-centric agentic intelligence
Augmenting a network graph with agentic AI produces a “digital twin” that can help isolate network failures.
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A kernel-centric path to real-time video generation on Trainium
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 te...
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Amazon launches research initiative with Stanford University to advance AI and science
The collaboration aims to advance research while broadening participation and translating discovery into real-world solutions.
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Advancing AI for biology: Teaching models to design and characterize antibodies
Three new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design.
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Why don’t machine learning research agents overfit?
New research indicates that AI agents learn compressible models of data, which don’t have enough space to enable memorization.
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Developing provably correct Rust code with Verus
How the Verus "program verifier", which automatically checks code against a mathematical specification of its functionality, helps increase security assurance in software projects.
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When LLM judges agree, should we believe them?
Discounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives.
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SOP-Bench: A new benchmark for evaluating AI agents on real business procedures
Extendable framework enables testing agents on the full set of capabilities required to successfully complete a procedure, not isolated proxy tasks.
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A decade of mathematical certainty: Reflections on the Automated Reasoning Group
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 tha...
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AWS Trainium Frontier competition: Co-design models and kernels on purpose-built AI chips
A competition with a finalist ceremony during NeurIPS 2026, challenging researchers to train language models from scratch on Trainium, exploring what optimal architectures look like when the hardwa...
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34 Amazon Research Awards Build on Trainium recipients announced
Amazon announces 34 recipients of the Build on Trainium program, a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT...
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How controllers from industrial machinery can coordinate multitask machine learning
Instead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically.
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A new benchmark for evaluating patient-facing health AI agents
PatientAgentBench generates a synthetic patient health record, a realistic clinical vignette, and a patient agent that converses with the AI system under evaluation, to capture what a patient-facin...
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Amazon is investing in the Lean Focused Research Organization
As AI agents take on higher-stakes decisions, Lean programming language makes it possible to mathematically prove they will behave safely.
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Amazon and University of Michigan give robots a sense of touch
HydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world.
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Capturing token IDs during agentic interactions for better reinforcement learning
A new Rust proxy called Turnstile sits between the model backend and the agent harness to capture information lost in mere text transcripts.
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How Amazon tracks carbon intensity across its operations
Amazon is developing precise, sector-specific approaches to measuring decarbonization progress — starting with emissions per unit shipped.
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The fuel of the future is already here: Why TRISO matters
Millimeter-scale particles of nuclear-reactor fuel are encased in four layers of different materials that act as a “miniature containment system”.
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Graviton5’s improved design increases speed and energy efficiency — beyond Moore’s law
A new chiplet architecture, custom die-to-die connectivity, and support for DDR5-8800 memory and the latest PCIe gen6 interconnects improve performance by 25% for general-purpose and agentic AI wor...
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EC2’s formally verified “isolation engine” provides mathematical assurance of virtual-machine isolation
Splitting the “separation kernel” off from the rest of the Nitro security system and using only a subset of the Rust programming language to code it enabled its formal verification.
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Real-world grounding in agentic AI
Four approaches can dramatically improve the performance and trustworthiness of AI agents in operational environments.
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Bridging intent and execution in agentic systems
The harnesses that mediate between models and tools in agentic systems are becoming their own performance bottleneck, but a few simple design principles can fix what ails them.
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Ground truth is a process, not a dataset
Automatically fact-checking long, AI-generated research reports poses new challenges — including benchmarking.
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How flat is replacing fat in AWS data center networks
“Quasi-random” network topologies and new passive optical components called ShuffleBoxes make more-efficient flat networks as practical as traditional “fat-tree” networks.
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Amazon Research Awards recipients announced
Awardees represent more than 49 universities in 11 countries. Recipients have access to Amazon public datasets, along with AWS AI/ML services and tools.
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Diverse reasoning traces teach LLMs to make better decisions
How to train language models to generate diverse, accurate reasoning paths using tokens that control distinct reasoning strategies.
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Making LLMs faster without sacrificing accuracy
A new scaling law that relates particular architectural choices to loss helps identify models that improve throughput by up to 47% with no loss of accuracy.
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Promptimus: Improving already good LLM prompts with zero manual engineering
By focusing on specific failure points and suggesting targeted solutions, a new automated prompt-engineering framework improves prompt performance without compromising existing functionality.
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Navigating uncertainty in Amazon's middle-mile network
Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty — and keep them adapting without missing a beat.