Amazon Science✓

📣 Amazon Research Awards fall 2026 call for proposals is now open for submissions. Successful applicants will receive unrestricted funds, AWS promotional credits, and training resources. Research areas: • Agentic AI • AI for Information Security • Amazon Security • Automated Reasoning • AWS Cryptography • Build on Trainium • Multilingual and Contextual AI Evaluation • On-Device Multimodal Perception • Synthetic Data Generation for Multimodal Perception Deadline: November 4 #AmazonResearchAwards

RT @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.

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:

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.

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.

RT @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

RT @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.

RT @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.

RT @PyTorch: PyTorch runs natively on @awscloud Tranium with no code changes. AWS Tranium is @amazon's portfolio of AI chips designed for next-generation deep learning training and inference workloads. You can use it with the tools and workflows you are already familiar with. No new frameworks to learn. No code to change. During PyTorch Conference North America keynote, Robert Hundt, VP Distinguished Engineer, Annapurna Labs, Amazon will share the story and the capabilities AWS provided to meet performance and quality goals. @AmazonScience #PyTorchCon

RT @amazon: Introducing TorchNeuron, a new open-source tool that lets developers take their PyTorch code and run it on @awscloud Trainium chips, no rewrite required. 85% of AI researchers and developers write their code in PyTorch, and now, with TorchNeuron, the tools developers already use to test, debug, and scale their models all work on Trainium without modification. Teams building everything from voice AI to drug-discovery models are already using it. ⬇️

RT @PyTorch: More hardware choice for PyTorch developers: TorchNeuron registers @awscloud Trainium as a native device, built on the same extension points the community created to make PyTorch hardware-agnostic. Open sourcing at PyTorch Conference on Oct. 20.

Announcing TorchNeuron: @PyTorch code now runs natively on AWS Trainium chips with a single configuration change. No rewrite needed. Open sourcing at #PyTorchCon on Oct 20:

A great week with the language modeling community at #COLM2026. Thanks to everyone who connected with our team. See you next year!

🎤 Can you out-rhyme the AI agent? Stop by the Amazon booth at @COLM_conf for a live AI rap battle demo, plus talks on multi-agent systems and responsible AI for science: #COLM2026

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

Amazon Scholar and @UTAustin professor @mattlease explores how to harness AI for scientific discovery while assessing potential risks, drawing on work from @UTGoodSystems and @CosmicAI_Inst. Catch his Expo Talk at @COLM_conf Thursday at 1pm PT. #COLM2026

Day 2 at @COLM_conf. Stop by the Amazon booth for talks on multi-agent orchestration, visual reasoning, and LLM agent evaluation: #COLM2026

RT @Perceptron97: At #COLM2026 this week! 👋 I’ll be at the Amazon Science booth for Meet the Scientist — come say hi and chat about LLM post-training, agentic post-training, multimodal learning, world models, video understanding, or anything AI/ML! 📍 Wed, Oct 7: 12–1 PM 📍 Thu, Oct 8: 1–2 PM

We're at @COLM_conf this week! Stop by the Amazon booth for live talks on super weights in LLMs, multi-agent orchestration, responsible AI, and more, plus meet-the-scientist sessions all week. #COLM2026 Full schedule:

Day 1 at @COLM_conf. We're here all week with live research talks, meet-the-scientist sessions, and conversations on what's next for language models. Stop by the Amazon booth. #COLM2026 More info:

This week, Amazon researchers are presenting work at @COLM_conf across agentic AI, constrained decoding, retrieval optimization, and LLM code generation. #COLM2026