Amazon Science

The latest news and research from Amazon's science community. #AmazonScience

[Amazon Science @AmazonScience]

Congratulations to the Amazon Last Mile team on winning the 2026 @INFORMS Daniel H. Wagner Prize. Their paper details a planning system that accounts for how temperature and terrain change a delivery EV's energy needs. https://t.co/Y3AAPy3vxf ⚡

[Amazon Science @AmazonScience]

The SGLang workflow you already know now runs on AWS Trainium.

SGLang-Neuron: serve models and generate RL rollouts on Trainium — same server, same APIs, native PyTorch through TorchNeuron. @sgl_project #SGLangSummit https://t.co/SlJDFnaisy

[Amazon News @amazonnews]

https://t.co/3v03ZynVxh

[INFORMS @INFORMS]

Press Release: Researchers from @Amazon awarded INFORMS 2026 Daniel H. Wagner Prize [INFORMS Newsroom] 🔗 https://t.co/n6KwSvctgs

[Amazon Science @AmazonScience]

Introducing Mitra-v2, a tabular foundation model for both classification and regression. Pretrained entirely on synthetic tables, it leads the single-model field on the TabArena benchmark. https://t.co/LZ9xpIyWnc

[Amazon Science @AmazonScience]

📣 Last call: Amazon Research Awards Fall 2026 proposals are due November 11. Successful applicants receive unrestricted funds, AWS promotional credits, and training resources. https://t.co/phBWidoDeE

[Amazon Science @AmazonScience]

Amazon senior applied scientist Hamed Karimi received the 2026 @ECMLPKDD Test-of-Time Award for his 2016 paper on the Polyak–Łojasiewicz condition. #ECML2026 https://t.co/rYpf8RTDbR

[Amazon Science @AmazonScience]

How do you keep 70-billion-parameter quality while running up to 20 times as fast? AWS researchers combined knowledge distillation with parallel decoding. For @bookingcom's Smart Filters, that meant responses 15 times as fast for 99% of queries. https://t.co/jRQcygQbHX

[Amazon Science @AmazonScience]

A text-to-speech system that knows which character is speaking, and voices them accordingly. @audible_com's controllable TTS profiles characters and attributes dialogue with 97% accuracy in English: https://t.co/5ejzH2vJNT

[The Sanghani Center at Virginia Tech @SanghaniCtrVT]

A 2026-27 @amazon Fellowship has been awarded to @VT_CS computer science Ph.D. student @Hanibalomari. The Amazon-VT initiative is also supporting five faculty projects. Read about their research at 🔗https://t.co/FQOB4i4mJD

@AmazonScience
@virginia_tech
@SanghaniCtrVT

[Amazon Science @AmazonScience]

📢 The Amazon Research Awards Fall 2026 deadline has been extended to November 11.

Award recipients receive unrestricted funds, AWS promotional credits, and training resources. Submit your proposal today: https://t.co/TqH3cofT6a

[Amazon Science @AmazonScience]

Amazon's custom Trainium chip helps power the diffusion models behind Prime 360's cinematic highlights, reducing diffusion-model inference latency by more than 40%: https://t.co/QZjc3zgx3X

[PyTorch @PyTorch]

Most AI developers use PyTorch to build their models. And this week @AmazonScience announced a new PyTorch backend for AWS Trainium chips - TorchNeuron. Learn more about the science behind it.

[Amazon Science @AmazonScience]

TorchNeuron is now open source. Our new @PyTorch 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: https://t.co/czPvqSPAcR

[Amazon Science @AmazonScience]

@zheyuanzhang99 @amazon Congratulations Brian!

[Amazon Science @AmazonScience]

Same quality, twice the throughput, half the memory. Hybrid models increase LLM efficiency with mechanisms like sparse attention or state space layers. New AWS research introduces "priming," which reuses pretrained Transformer weights to build these hybrids at less than 1% of the original training cost. 8B and 32B models released. https://t.co/qvG8Yx6jcK

[Amazon Science @AmazonScience]

The true root cause may generate no alarm at all. New work augments a network graph with agentic AI to produce a "digital twin" that isolates failures in minutes, not hours. https://t.co/2HdWhD1JOr

[PyTorch @PyTorch]

Live from #PyTorchCon 2026, Robert Hundt (@Amazon) introduces TorchNeuron, the native PyTorch backend for @awscloud Trainium.

Robert shows how TorchNeuron supports eager execution and torch.compile, letting developers train on Trainium using familiar PyTorch code and distributed training APIs.

He also covers how vLLM-Neuron uses torch.compile for serving, and how Neuron Explorer lets developers profile execution from PyTorch down to hardware instructions. @AmazonScience

[Matt Garman @mattsgarman]

We're open sourcing TorchNeuron. Your existing @PyTorch code runs on Trainium with one config change, and we're contributing upstream to make PyTorch more hardware-agnostic for everyone. https://t.co/Kpq6s2kz81

[Andy Jassy @ajassy]

Trying a new AI chip used to be a lot of work. You often had to rewrite your code, revalidate it, and sink many weeks in, betting this new chip is the right one for your workload.

That cost time and money, and wore on the team. Your best people got buried in plumbing instead of building the thing they came to build.

TorchNeuron is going to largely take that off the table, enabling your existing PyTorch code to run natively on Trainium with one configuration change and no rewrite.

With Trainium’s very appealing price-performance characteristics, TorchNeuron is going to make it even easier for more companies to use AI more cost-effectively. https://t.co/TgNr3p71vw