The method analyzes network topology statically every hour to prove isolation rules hold across all modeled paths, replacing impractical dynamic packet sampling.
Mitra-v2, a second-generation tabular foundation model, is pretrained on tables with 10x more rows and 3x more columns than its predecessor, leading TabArena and ScoringBench.
The 2016 paper by Karimi, Nutini, and Schmidt established that the Polyak–Łojasiewicz condition guarantees linear convergence for standard training methods, bridging a gap between theory and practice.
The combined approach matches the quality of a 70-billion-parameter teacher model while running 10 to 20 times faster and at less than one-third the cost.
The system uses BookLLM to profile characters with 97% accuracy in English and 94% in Spanish, enabling distinct and consistent voices across full-length books.
The open-source backend uses AI agents to benchmark 841 representative models, ensuring accurate execution and reducing time to the first training step by about a third.
The review confirmed comprehensive topical coverage and low contamination risk while identifying gaps in linguistic diversity and attack sophistication.
The method reduces pretraining costs by over 99% and enables controlled comparisons of hybrid architectures like Gated KalmaNet, Gated DeltaNet, and Mamba-2.
Demonstrated with NTT DOCOMO, the approach reduces root cause analysis from hours to minutes on commercial networks.
The collaboration reduced 3D-RoPE kernel latency from 5 seconds to 1.8 milliseconds, enabling real-time generation above 16 fps.
Amazon has officially launched an initiative for joint research in AI, energy, and healthcare with Stanford University.
The Amazon Bio Discovery team released three AI-based antibody research papers covering binding strength prediction, developability assessment, and novel drug design.
LLM research agents generalize without benchmark overfitting because their successful strategies possess high compressibility.
Amazon uses Verus to mathematically prove the correctness of Rust code, providing guarantees that go beyond Rust's existing safety features.
A new aggregation method accounting for correlations among LLM judges improved accuracy by 9-14% over existing methods.
SOP-Bench, a benchmark combining real-world corporate SOPs and tools to verify AI agents' task execution capabilities, has been released.
Amazon's Automated Reasoning Group reflects on its past decade of research and achievements.
AWS optimizes AI models and kernels alongside chip design to strengthen Trainium's competitiveness.
Amazon is providing Trainium research support to 34 AI research teams from 30 universities.
ControlG applies industrial PID control principles to efficiently coordinate conflicting multi-task learning objectives.
A new benchmark called PatientAgentBench has been released to measure the safety and clinical workflow performance of patient-facing AI agents.
Amazon and the University of Michigan have improved robots' ability to learn tactile sensing through the physics-based simulator HydroShear.
To reach net-zero by 2040, Amazon is introducing carbon intensity metrics tailored to each business unit, including carbon emissions per unit shipped.
Amazon is investing in TRISO, a next-generation nuclear fuel technology, to meet the growing power demands of AI infrastructure.
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