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Amazon
Technology and commerce company operating online retail, cloud computing, digital services, logistics, entertainment, and other businesses.
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Amazon ·
Graviton5’s improved design increases speed and energy efficiency — beyond Moore’s law
Amazon announced the general availability of M9g and M9gd EC2 instances powered by the new Graviton5 processor. Built on a three-nanometer process, Graviton5 features 192 Neoverse V3 cores, DDR5-8800 memory support, PCIe gen6 interconnects, and 192 megabytes of level-three cache. The chip transitions from seven discrete dies to four unified chiplets connected at 420 gigabytes per second, eliminating dedicated I/O and memory controller dies. These architecture updates deliver up to 25% higher computational performance over Graviton4, with up to 35% faster performance for web applications and machine learning inference. The new instances also introduce the Nitro Isolation Engine, which uses formal verification to enforce hardware and software isolation between virtual machines.
Ali SaidiAmazon ·
Real-world grounding in agentic AI
Foundation models deployed as physical AI agents face critical risks when natural language hallucinations translate into physical law violations and operational hazards. To ensure physical consistency and reliability, researchers from the University of California, San Diego and Amazon Fulfillment Technology introduced four grounding pillars. Physics-guided deep learning embeds symmetries and differential equations into pretraining to obey conservation laws while reducing dataset requirements. The UQ4CT framework applies mixture-of-experts architectures to produce calibrated uncertainty estimates, cutting expected calibration error by over 25% and halting operations when safety thresholds are breached. Furthermore, the Adapting-While-Learning framework and formal verifiers like Hilbert integrate numerical simulators and interactive proof systems to enforce mathematical and scientific correctness.
Rose YuAmazon ·
Bridging intent and execution in agentic systems
AI agent performance increasingly depends on the harness mediating interactions between language models and tools rather than raw model reasoning. To address mismatches between model intent and harness execution, the authors developed Simple Strands Agent, an open-source single-agent harness. The architecture implements robust tool-interface practices, such as condensing middle log outputs from bash commands, enforcing line-aware string replacements with ambiguity checks, and returning diff files after edits for verification. Evaluated across benchmarks like SWE-Bench-Verified, SWE-Bench-Pro, and Terminal-Bench 2, these model-harness alignments yielded consistent performance gains across frontier models without task-specific tuning. The findings show that model families require tailored interface codesign alongside stable evaluation infrastructure to achieve optimal benchmark reliability.
Gaurav Gupta, Vatshank ChaturvediAmazon ·
Ground truth is a process, not a dataset
Evaluating search-augmented AI research reports requires verifying complex, multi-source claims that depend heavily on surrounding context. Unassisted PhD-level domain specialists tasked with establishing static ground-truth labels achieved only 60.8% accuracy on a hidden test set during controlled testing. To resolve benchmark errors on cognitively demanding tasks, Amazon AGI introduced the audit-then-score evaluation protocol alongside DeepFact-Bench and the DeepFact-Eval verification system. Under this protocol, model disagreements serve as challenges requiring concrete counterevidence and written rationales, enabling human auditors to evaluate competing claims and update benchmark ground truth prior to scoring. Operating under this iterative workflow, expert test accuracy rose to 90.9% across four rounds, and DeepFact-Eval reached 83.4% accuracy with GPT-4.1.
Venkatesh SaligramaAmazon ·
How flat is replacing fat in AWS data center networks
Traditional data center networks rely on hierarchical fat-tree topologies, which introduce overhead, upper-layer congestion, and vulnerability to router failures. Although flat random networks theoretically maximize route diversity and resilience, routing computation and cabling complexity historically made them impractical for physical data centers. AWS addressed these constraints by introducing Resilient Network Graphs, a quasi-random architecture powered by a passive optical device called ShuffleBox and a routing algorithm named Spraypoint. Spraypoint sprays packets across random neighbors toward designated waypoints, unlocking twice as many independent paths while operating within commodity router memory limits. Adopted as the default architecture across new AWS facilities, this design reduces router counts by 69%, lowers projected network power consumption by 40%, and increases throughput by up to 33%.
Giacomo Bernardi, Ratul Mahajan, Seshadhri ComandurAmazon ·
Amazon Research Awards recipients announced
Amazon Research Awards has announced 70 award recipients across 49 universities in 11 countries for its fall 2025 funding cycle. The funded research spans six distinct tracks: AI for Information Security, Agentic AI, Automated Reasoning, AWS Cryptography, Cybersecurity and Anti-Abuse Technologies, and Sustainability. Each selected academic team receives unrestricted financial grants, AWS Promotional Credits to access specialized cloud AI and machine learning services, and access to more than 700 Amazon public datasets. Awardees are paired with an Amazon research contact who provides advisory support, consultation, and access to company training sessions. Furthermore, the program explicitly encourages recipients to publish their discoveries, deliver presentations at Amazon offices worldwide, and release associated software implementations under open-source licenses.
Amazon Research Awards teamAmazon ·
Diverse reasoning traces teach LLMs to make better decisions
Large language models typically rely on supervised fine-tuning with a single human-verified reasoning trace per example, which limits reasoning breadth and causes mode collapse when attempting parallel reasoning. Researchers presented set-supervised fine tuning (SSFT) and global forking policy optimization (GFPO) to teach models diverse problem-solving strategies. SSFT represents reasoning as a set of complete solution paths paired with global forking tokens, utilizing bipartite matching to achieve order-invariant training and specialized tokens. GFPO applies lightweight reinforcement learning to select the optimal forking token upfront, detaching gradients from generated rollouts to ensure stable optimization. On standard reasoning and coding benchmarks, the combined framework increased single-shot accuracy by 5% to 7% while improving pass@k diversity without degrading pass@1 accuracy.
Sheng Jia, Xiao Wang, Shiva KasiviswanathanAmazon ·
Making LLMs faster without sacrificing accuracy
Standard scaling laws optimize parameter count and training data budgets to minimize loss but ignore internal Transformer architecture configurations. To address throughput disparities among equally sized models, researchers developed a scaling framework incorporating hidden size, the multilayer perceptron to attention parameter ratio, and grouped-query attention. The method calibrates a correction surface over standard Chinchilla scaling laws to independently optimize separable architectural parameters for target accuracy. Evaluated across more than 200 models up to three billion parameters, the framework produced the Panda model family for maximum accuracy and the Surefire family for Pareto efficiency. Surefire models matched or exceeded LLaMA-3.2 accuracy while increasing inference throughput by 12% to 47% across vLLM and SGLang serving systems on modern GPUs.
Tao Yu, Youngsuk ParkAmazon ·
Promptimus: Improving already good LLM prompts with zero manual engineering
Enterprise applications rely on mature large language model prompts that incorporate intricate business logic and regulatory constraints, making further optimization and cross-model migration challenging. Promptimus automates prompt refinement through a four-step iterative loop that evaluates candidate prompts against decomposed metric checkpoints, diagnoses failure bottlenecks, and generates targeted improvement strategies. The system operates either via full prompt rewrites in standard mode or through surgical, programmatic find-and-replace edits in edit mode to preserve structured taxonomies and schemas. Tested across multimodal classification and code generation tasks, the framework achieves performance gains with small development datasets of 20 to 50 samples. Amazon is making the model-agnostic optimization system available through Amazon Bedrock to support enterprise generative-AI model migration without manual engineering.
Zhengyuan Shen, Yunfei Bai, Sullam Jeoung, Shuai WangAmazon ·
Navigating uncertainty in Amazon's middle-mile network
Amazon's middle-mile logistics network coordinates tens of millions of packages across hundreds of facilities while managing subtle demand variations and major operational disruptions. To solve this mixed-integer optimization challenge without combinatorial explosion, Amazon models routes at coarse time resolutions augmented by precomputed 15-minute timing bounds. The system uses Monte Carlo methods alongside a graph attention network featuring interconnected site graphs and origin-destination graphs to capture complex spatial correlations and flow interdependencies. By generating synthetic flow permutations, planners stress-test candidate network designs against hundreds of plausible scenarios to prioritize routing optionality. This risk-aware modeling approach preserves network flexibility, absorbs severe shocks, and yields potential cost savings while safeguarding delivery promises.
Ruth Misener, Hana Ku, Georgios PaschosAmazon ·
How mechanism design theory helps optimize Amazon-vendor collaboration
Amazon and its vendors face an asymmetric information problem when coordinating purchase orders across fulfillment centers because neither party shares proprietary cost, capacity, or demand data. To solve this coordination gap without exposing sensitive information, Amazon's Supply Chain Optimization Technologies organization designed Flo Pro by integrating the Vickrey-Clarke-Groves mechanism with the Consensus Planning Protocol. The distributed optimization framework uses the alternating-direction method of multipliers to coordinate iterative agent responses, calculating cost-benefit transfers across rolling six-week planning horizons. In lower-dimensional settings, Amazon also evaluated a transparent menu-of-contracts approach where vendors select optimal plans priced at Amazon's incurred costs. A nine-week pilot with a consumer-product manufacturer confirmed that Flo Pro successfully aligns incentives and achieves measurable cost savings.
Dirk Bergemann