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…
Four approaches can dramatically improve the performance and trustworthiness of AI agents in operational environments.
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…
Automatically fact-checking long, AI-generated research reports poses new challenges — including benchmarking.
“Quasi-random” network topologies and new passive optical components called ShuffleBoxes make more-efficient flat networks as practical as traditional “fat-tree” networks.
Awardees represent more than 49 universities in 11 countries. Recipients have access to Amazon public datasets, along with AWS AI/ML services and tools.
How to train language models to generate diverse, accurate reasoning paths using tokens that control distinct reasoning strategies.
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.
By focusing on specific failure points and suggesting targeted solutions, a new automated prompt-engineering framework improves prompt performance without compromising existing functionality.
Amazon engineers and scientists have created new tools to optimize delivery networks under uncertainty — and keep them adapting without missing a beat.
