Amazon Science

Amazon Science

@amazon.science

The latest news and research from Amazon's science community. http://www.amazon.science

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TorchNeuron is now open source. Our new @pytorch.org backend translates code into operations executable on AWS Trainium with minimal or no code changes. Learn how the team used AI agents and hundreds of thousands of models to validate it.

amazon.scienceThe science behind TorchNeuron, Amazon’s new PyTorch backend for AWS Trainium chipsRuntime 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.

New AWS research introduces priming, a method that reduces pretraining costs for hybrid architectures by over 99% by leveraging pretrained Transformer weights, saving trillions of tokens and hundreds of thousands of GPU-hours.

amazon.scienceCan we change an AI model's architecture without retraining it from scratch?“Priming” reuses pretrained Transformer weights to build models with more efficient architectures at less than 1% of the original training cost and with throughputs up to twice as high.

AI-powered smart glasses that guide delivery drivers from van to the customer's doorstep, hands-free. Hear from Amazon applied scientist Yelin Kim on the computer vision and edge AI behind it at @colmweb.org. #COLM2026.

"One workload is power-bound. The next workload is memory-bandwidth-bound. The next is memory-bound. It's one of the most interesting hardware design problems that we've seen in ages." Amazon SVP Peter DeSantis sat down with SemiAnalysis founder Dylan Patel at #AIInfraSummit:

aboutamazon.comInside a decade of Amazon chip design, built workload by workloadAt AI Infra Summit, Amazon SVP Peter DeSantis explained how real customer workloads guided every generation of Amazon's chips, from Nitro to Trainium.

Amazon Bio Discovery developed three AI approaches to accelerate antibody drug design: MochiBind (sequence-based affinity ranking), CA-MAP (developability prediction with batch effect correction), and an agent-guided design system with 46 lab-validated hits against a novel cancer target.

amazon.scienceAdvancing AI for biology: Teaching models to design and characterize antibodiesThree new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design.

Years of iterating against the same benchmarks should, by textbook logic, produce overfitting. It largely doesn't. New research explains why: strategies that generalize can be expressed in too compact a form to allow memorization, while the ones that overfit don't survive a compression.

amazon.scienceWhy 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.

When LLM judges agree, the right question is why. Shared prompts, model families, or training lineage can make a majority look stronger than it is. Dependence-aware aggregation via Ising models accounts for this, improving accuracy 9–14% over weighted majority vote.

amazon.scienceWhen 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.

Amazon's Automated Reasoning Group started by demoing tools to prove AWS systems secure and correct. A decade later, they have proved the Nitro Isolation Engine, cryptographic code, and S3 correct. Now they are applying the same techniques to AI.

amazon.scienceA decade of mathematical certainty: Reflections on the Automated Reasoning GroupTen 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 s...

Amazon Science reposted

🎉 Congratulations to A/Prof. Wei Bao, one of the 34 recipients of the Amazon "Build on Trainium" Research Award! Wei will explore the Trainium architecture for "FACTOR: Federated Adversarial Co-Training with Textual Gradient for LLM Security and Robustness." www.amazon.science/research-awa...

amazon.science34 Amazon Research Awards Build on Trainium recipients announcedAmazon 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, w...

Training a graph neural network on multiple objectives usually means blending conflicting gradients at every step. Instead of compromising among parameter updates from different training objectives, ControlG allocates capacity to objectives sequentially and dynamically via PID control. #ICML2026

amazon.scienceHow controllers from industrial machinery can coordinate multitask machine learningInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically.

🎉 The Chronos family of models has reached 1 billion downloads on Hugging Face: https://amzn.to/3YBkfZl Chronos-2 handles univariate, multivariate, and covariate-informed forecasting in a zero-shot manner, outperforming existing time series foundation models by a substantial margin.

amazon.scienceIntroducing Chronos-2: From univariate to universal forecastingIn-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.

What a week. #ICML2026 brought together some of the best minds in machine learning, and we were proud to be part of it. Thank you to everyone who joined us in Seoul. See you next year!

The biggest AI breakthroughs are still ahead of us. At @vivatech.bsky.social, Peter DeSantis, Amazon’s head of AI, chips, and quantum, said transformers won’t be the last architecture, and that chips and models have to evolve in lockstep to get there.

aboutamazon.comAmazon's AI SVP on why AI's biggest breakthroughs haven't happened yetAt VivaTech 2026 in Paris, Peter DeSantis argued we're at the starting line of AI development with the most important breakthroughs still ahead.

Amazon researchers release Simple Strands Agent (SSA), a lightweight, open-source harness that achieves SOTA across SWE-Bench-Verified, SWE-Bench-Pro, and Terminal-Bench2 by closing the gap between what models intend and what harnesses execute.

amazon.scienceBridging intent and execution in agentic systemsThe 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.

Don't miss what's happening at our #CVPR2026 booth the rest of the week. Live demos, technical deep dives, and conversations with our researchers – here's the full schedule:

Amazon Science reposted

Today is CVPR Denver with AWS/Amazon. And it's all about Computer Vision. Something I haven't spent a lot of time with but decided to see how I could apply #serverless. Here is what I came up with. Check it out & if you're at CVPR come see me at the Amazon booth after 2! edjgeek.com/blog/9-tiles...

edjgeek.comFrom 9 Tiles to 900: Scaling Computer Vision Pipelines — edjgeekComputer vision pipelines are scale problems disguised as orchestration problems. You can solve both with a single function.