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The latest news and research from Amazon's science community. #AmazonScience
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Joined December 2019
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Using the Neuron Kernel Interface, @reactorworld and Amazon's Neuron Science team built a kernel-centric path to real-time autoregressive diffusion video generation on Trainium.
They tackled the dynamic shapes, memory access patterns, and cache management that make these workloads hard for generic compilers, and built techniques that generalize across models. amazon.science/blog/a-kernel…
"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 @dylan522p of @SemiAnalysis_ at #AIInfraSummit: aboutamazon.com/news/innovat…
Amazon Science retweeted
The most durable skill in the age of AI isn’t any single technology. It’s learning.
I was back at @Stanford last week celebrating the Stanford-Amazon Research Initiative, bringing our researchers together to work on hard problems across AGI, robotics, healthcare and more.
Excited to see what we learn and solve together.
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.science/blog/advancin…
Amazon Science retweeted
We spent tens of billions of tokens building a benchmark for AI security — so you don't have to.
Deception Benchmark is now open on GitHub: 14,822 samples, 16 languages, 70+ CWEs.
Download the dataset, run your tools, hold them accountable.
go.aws/4xUbAjP
#AWS #OpenSource
Amazon Science retweeted
"Machine learning, at its core, is about generalization, not memorization," write @awscloud Applied Scientist Martin Bertran Lopez and @WarrenCntrPenn faculty affiliate @Aaroth for @AmazonScience. amazon.science/blog/why-dont…
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 bottleneck. amazon.science/blog/why-dont…
Amazon Science retweeted
Trainium has by far the best profiler of any accelerator. As you can see we have nanosecond-accurate traces of our programs, allowing you to write a program that generates images reliably in the profiler.
.@Amazon and @DARPA brought together 150+ researchers in Seattle last week, with speakers including @awscloud CEO @mattsgarman, Fields Medalist @TaoistTerence, @EPrinceton Associate Professor @BorisHanin, and @NSAGov's Michael O'Hara to explore how AI is transforming mathematical discovery and reasoning.
Amazon Redshift researchers were awarded Best Paper Runner-Up: Industrial Track at @VLDBconf for eliminating compilation cold starts in query execution – cutting compilation time from seconds to milliseconds with a 7x speedup on TPC-DS benchmarks. #VLDB2026 amazon.science/publications/…
Verus is an open-source, automated program verifier for Rust that mechanically checks code against a formal mathematical specification for all possible inputs. Amazon used it to prove correctness of Nitro Isolation Engine primitives. amazon.science/blog/developi…
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.science/blog/when-llm…
Amazon Science retweeted
Honored to be named to @TIME's 2026 TIME100 AI list. This one belongs to our customers and the teams at @awscloud. Still early.
AWS CEO @MattSGarman has been named to @TIME’s 2026 TIME100 AI list. Congratulations, Matt. go.aws/4gFhle3
How did a model upgrade make agents worse? By pairing real enterprise SOPs with functioning tools and ground-truth grading across 12 industries and 2,000+ tasks, SOP-Bench helps find such anomalies. amazon.science/blog/sop-benc…
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.science/blog/a-decade…
📣 AWS Trainium Frontier is open for registration. Train language models from scratch on purpose-built AI chips for @NeurIPSConf.
Prizes include $25K for first place, co-publication with Annapurna Labs researchers, and a presentation in Sydney. Deadline is September 30. #NeurIPS2026 amazon.science/news/aws-trai…
Attending #KDD2026? Come meet Amazonians and fellow attendees at our mixer. Spots are limited – request to attend: luma.com/8xar6jx9
📣 Amazon Research Awards announces the 34 recipients of the Build on Trainium program, a $110M credit initiative supporting AI research at 30 universities on AWS Trainium: amazon.science/research-awar…
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.science/blog/how-cont…
Most healthcare AI benchmarks test static medical knowledge or evaluate tool-using agents on provider-facing tasks. PatientAgentBench generates synthetic patient records and clinical vignettes, then runs multiturn dual-agent conversations scored by an LLM-as-a-jury panel across over 100 clinician-vetted criteria. amazon.science/blog/a-new-be…
