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Only First party#Microsoft
Nov 14Sat
  1. AWS Machine Learning Blog24

    Production MLOps at GS Caltex with Amazon SageMaker Canvas, part 1

    GS Caltex connects models built by process engineers in Amazon SageMaker Canvas to production workflows in Amazon SageMaker Pipelines, with ML engineers handling deployment and scheduled inference. The company automated the handoff so ML engineers now deploy models within a few hours instead of about a week, and it runs batch inference every 30 minutes, which it reports has meaningfully lowered inference costs. The post details the reference architecture, including reusing Data Wrangler preprocessing flows, registering and approving models in Model Registry, and scheduling runs with Amazon EventBridge.

  2. AWS Machine Learning Blog38

    Cost attribution and controls for AI developer and productivity tools on Amazon Bedrock

    Amazon Bedrock records the IAM principal behind every inference call and publishes it to CUR 2.0 and Cost Explorer, so per-user and per-team spend can be queried without instrumenting individual tools. The post walks through configuring IAM Identity Center with an external IdP, session tags, a Bedrock permission set, tool configuration for Claude Code, Claude Desktop, Codex and ChatGPT Work, and Athena queries, then compares usage controls from AWS Budgets to gateway-level enforcement.

Nov 13Fri
  1. Microsoft Research22

    Microsoft Research Focus – Edition #98

    This edition of Microsoft Research Focus covers AI-powered universal translation, the resolution of the 31-year-old KLS conjecture in high-dimensional geometry, and a new approach to making AI coding agents faster and more efficient. It also highlights a research intern's journey at Microsoft Research Asia – Tokyo and the Latent Core Tokenizer, which produces more linguistically meaningful tokens across 104 languages.

  2. AWS Machine Learning Blog36

    Tokenomics at scale: Track and cap Amazon Bedrock spend per IAM principal with CUR 2.0 and AWS Budgets

    This AWS post describes how to get per-identity Amazon Bedrock cost visibility and enforceable spend limits using only AWS-native features: IAM principal and session tags for attribution, Cost Explorer or CUR 2.0 exports (with the caller identity option) for reporting, and AWS Budgets with budget actions that attach a Deny IAM policy when a threshold is crossed. It notes that budget data refreshes up to three times a day, cost allocation tags are not retroactive, and a Deny action on a shared role blocks everyone using it.

Nov 11Wed
  1. Microsoft Research78

    Lazare, veni foras: Reprogramming Dormant Neurons to Backdoor AI Agents

    Microsoft researchers describe "Lazarus," a backdoor technique that reprograms fewer than 10 dormant neurons in an LLM, without training, so an agent built on it behaves normally and passes standard evals until a trigger—such as a date change—causes it to download and run attacker code with whatever access it has. The attack needs only write access to a checkpoint, so internally developed models are equally exposed, and reliable detection requires diffing against original weights, making provenance and weight integrity critical. Microsoft Foundry ties open-weight models to their publishers and scans surrounding containers, and Microsoft is developing AI-powered weight scanning, a red-teaming harness, and cryptographic model signing.

    Why it matters: It offers a concrete, researcher-first account of a low-cost weight-space backdoor and the provenance and weight-auditing practices it implies for anyone deploying agents.

  2. AWS Machine Learning Blog36

    How TCS built an agentic test orchestrator with Amazon Bedrock AgentCore

    TCS built a multi-tenant Agentic Test Orchestrator on AWS that turns Jira stories, BRDs, Swagger specs, and GitLab sources into test scenarios, Gherkin files, and executable Java or Python tests, using specialized agents hosted on Amazon Bedrock AgentCore runtime with the Strands Agents SDK. The platform separates a shared control plane from per-tenant application planes with isolated Cognito user pools, KMS keys, S3 buckets, and DynamoDB tables, and routes API tests to Amazon ECS on Fargate and Selenium UI tests to an EC2-backed ECS cluster. TCS reports that in an internal benchmark of about 100 scenarios, average effort fell from roughly two hours to about 14 minutes per scenario, an 88 percent reduction.

Nov 10Tue
  1. AWS Machine Learning Blog32

    Professional work deserves professional tools. Meet AWS Quick.

    AWS announced its first brand campaign for AWS Quick, an AI workspace built only for work where teams use AI agents to research, analyze, and act across the data and applications they already rely on. The campaign runs in the United States and the United Kingdom on streaming TV, online video, social media, and leading business publications, and cites enterprises including 3M, BMW, Mondelez, New York Life, and Southwest Airlines as users. AWS says Quick has no consumer version and integrates with security and compliance tools such as Microsoft Purview, AWS IAM, and Microsoft Active Directory, and carries certifications including HIPAA, FedRAMP, SOC 2, and ISO 27001.

  2. AWS Machine Learning Blog38

    How CrossnoKaye grounds physical AI with Amazon Bedrock AgentCore

    CrossnoKaye's ATLAS software-defined automation platform and its AURA agentic system run on AWS, using Amazon Bedrock AgentCore code interpreter microVMs to execute model-authored Python for verifiable statistics and physical balances across plant data. AURA operates in three modes—interactive Chats with warm sandbox sessions, repeatable Tasks, and automated Routines—and enforces layered safety through Bedrock Guardrails, read-only air-gapped sandboxes, deterministic edge base control, and independent hardware protection. The post describes the architecture and reports results such as investigations completing in under four minutes and a 34 percent reduction in batch drying-time standard deviation.

Nov 6Fri
  1. Microsoft Research58

    Formally verifying agent-written full-stack web apps with PDD Kit

    GitHub Next describes PDD Kit, a toolkit for Proof-Driven Development that turns a developer's intent into a formal specification in Verus, proves the agent's code meets that spec for every input, and falls back to tests where proofs cannot reach. The team applied it to Pet Store, a roughly 5,000-line Rust full-stack app, formally verifying its login logic while the developer reviewed the intent rather than reading generated code line by line. PDD Kit labels each result as either formal verification or empirical evidence and asks developers to sign off on each trusted component.

Nov 4Wed
  1. Microsoft Research20

    Learn the rules: The hacker mindset

    In a Microsoft Research Podcast conversation with Weishung Liu, research manager Mike Walker describes how hacker culture led him to a career in cyber defense, including leading DARPA's Cyber Grand Challenge, the first all-machine hacking tournament, which marked its 10th anniversary in 2026. He reflects on why effective security depends on understanding trust and context, what competitive hacking taught him about teamwork, and whether today's networked systems are safer than a decade ago.

  2. AWS Machine Learning Blog28

    Introducing India-based inference for Kimi K3 on Amazon Bedrock

    Amazon Bedrock now supports in-country inference in India for Moonshot AI's multi-modal, open-weight Kimi K3 model via the in.moonshotai.kimi-k3 geographic inference profile, processing requests in the Mumbai and Hyderabad Regions for workloads with strict data residency requirements. The post explains global versus India inference profiles and walks through an example of OCR and document understanding for Indic-language documents, including structured extraction and follow-up questions in code-mixed Hinglish. It also covers prompt caching, service tiers, and CloudWatch monitoring, citing Ultrahuman's report of 80 percent lower LLM costs and 85 percent faster multi-turn chats with prompt caching.

Oct 31Sat
  1. AWS Machine Learning Blog36

    Managing Amazon Bedrock costs end to end: A playbook for FinOps and platform teams

    This AWS post lays out a five-stage, looped operating model for Amazon Bedrock cost management: attribute every dollar to an owner via IAM principal and session tags in CUR 2.0, make every request observable through model invocation logging and stamped request metadata, enforce budgets at one of three tiers (monitor-only, IAM deny on breach, or synchronous gateway blocking), optimize platform defaults such as intelligent prompt routing and prompt caching, and operate a scorecard with a review cadence. It notes that agentic workloads issue many invocations per task and scale token consumption with autonomy and context size rather than user interactions, and it addresses the shared-role attribution problem in LLM gateways. FinOps leads the attribution and operate stages, platform engineering leads observability, enforcement, and optimization, and the review is joint.

Oct 30Fri
  1. Microsoft Research40

    From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence

    Microsoft describes its Industry Evaluation Data Generation Platform, which builds fully synthetic enterprise environments grounded in public sources such as O*NET occupational data, job postings and SEC filings to evaluate Microsoft Copilot without using customer data. The platform combines synthetic tenants across pharmaceutical, capital markets and IT/software industries with an automated six-stage query generation pipeline and quality frameworks covering structural fidelity, contextual relevance, behavioral realism and content safety. Microsoft reports a 39% improvement on its composite synthetic data quality metric versus its initial approach, some industry environments scoring above 0.9 out of 1.0, and estimated savings of about $100,000 per evaluation dataset and $300,000 in total project savings.

  2. Microsoft Research22

    Microsoft Research Focus – Edition #97

    This edition of Microsoft Research Focus, hosted by Sowbhagya Suresh, highlights recent Microsoft research including ForkPilot, a self-improving agentic framework that reduces AI processing demands by up to 59%, and CABRA, a framework showing that understanding unfamiliar codebases remains a major obstacle for coding agents. It also covers TeleTune for learning agent skills from activity logs, findings that common Copilot uses may mask differences across industries, a Shiksha Copilot study in Indian schools, the Probabilistic Bias Correction framework for extended-range weather forecasting, and the public preview of the BioEmu2 biomolecular model.

Oct 27Tue
  1. AWS Machine Learning Blog34

    Fleet-wide computer vision lifecycle at the edge with SageMaker SDK v3

    This post updates a 2023 MLOps pipeline pattern for visual quality inspection to Amazon SageMaker Python SDK v3, replacing the deprecated SageMaker Edge Manager with SageMaker Neo for edge optimization. It describes a dual-branch SageMaker Pipeline that trains an anomaly detector and a defect classifier per camera, gates models on their exported ONNX artifacts, compiles them with Neo, and registers them with camera metadata, while a Step Functions and DynamoDB semaphore layer paces fleet training. The sample deploys via AWS CDK and a full synthetic-data run costs about $0.07 in compute.

Oct 23Fri
  1. Hugging Face Blog62

    Turning Database Grading into RL Rewards: Post-Training Agents with ThinkingBox

    Microsoft and Hugging Face describe using ThinkingBox's executable terminal-database-state checks as binary rewards to fine-tune Qwen3.8-27B with GRPO for 50 steps on an offline-selected pool of 187 tasks, raising pass@1 from 51.70% to 60.78% and tasks passing all 20 attempts from 38 to 107 on the 507-task ThinkingBox-Bench, while pass@20 dipped slightly from 453 to 446. The post details offline policy-relative difficulty selection via Jeffreys posteriors, the reward loop, cost-per-dependable-task falling from $24.36 to $8.00, and releases the training stack in microsoft/thinkingbox-training.

    Why it matters: It offers a concrete, reproducible recipe for converting stateful agent grading into RL rewards, plus an honest account of the repeatability-versus-coverage tradeoff.

  2. AWS Machine Learning Blog58

    Announcing new ways to customize models with SageMaker AI, without managing GPUs

    AWS announced the general availability of training sessions in Amazon SageMaker AI serverless model customization, letting customers write and coordinate their own post-training loop, including custom loss functions and reward signals, while SageMaker AI handles GPU allocation, distributed execution, fault recovery, and checkpoint storage. The sessions support techniques such as SFT, DPO, RLVR, RLAIF, and multi-turn RL, with LoRA or full fine-tuning, and are available with support for gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B, and Qwen3.8-27B. Customers can start via the SageMaker Python SDK and cookbook or in SageMaker Studio, with data kept in their own S3 buckets and IAM, KMS, and VPC controls.

  3. Microsoft Research58

    Flint-interactive: A language for creating interactive data visualizations

    Microsoft Research introduces Flint-interactive, a language built on Flint that lets agents and developers create interactive data visualizations using high-level semantic events and update operators rather than low-level visualization code. Flint's compiler resolves raw triggers like clicks and drags into semantic events and translates update operators into rendered canvas changes, aiming to improve reliability and reduce generation cost. It supports presets such as linked-brush, a programming API for bespoke interactions, and connections between charts, tables, text, and application state.

Oct 22Thu
  1. Microsoft Research78

    Solving a 31-year-old grand challenge in mathematics – the KLS conjecture

    Microsoft researcher Xinzhi Zhang and a collaborator proved the Kannan–Lovász–Simonovits (KLS) conjecture, proposed in 1995, showing that for log-concave distributions the best hyperplane cut is within a universal constant of the best cut of any shape regardless of dimension. The proof emerged from a human-directed agentic process exploring more than 100 candidate approaches, and was followed by independent parallel proofs and a Lean 4 formalization. The authors say unguided attempts with leading AI models using a similar token budget fell short, while the success depended on both AI capabilities and researchers' mathematical judgment.

    Why it matters: It documents a major open problem's resolution and offers a concrete account of how human-guided AI search contributed to a mathematical proof.

Oct 21Wed
  1. NVIDIA Blog58

    NVIDIA Confidential Computing Expands, Enabling Frontier Model Builders to Securely Scale Across Global Infrastructure

    NVIDIA is expanding its Confidential Computing portfolio with Confidential Bare Metal, a deployment option that lets frontier model builders run sensitive workloads on dedicated third-party infrastructure while retaining control of their models and data. The offering joins Confidential Containers and Confidential Virtual Machines in a converged security architecture, with support extending to NVIDIA Grace Blackwell NVL72, HGX Rubin NVL8 and DGX Rubin NVL8 systems. Cohere plans to integrate Confidential Bare Metal into Cohere Model Vault, and Google Cloud is extending support to A4X Max instances powered by NVIDIA GB300 NVL72 systems.

  2. Microsoft Research12

    Called to serve: Tech, research, and positive impact

    In this Microsoft Research Podcast episode, principal technical program manager Weishung Liu interviews Chris White, director of Microsoft Research's Catalyst Lab, about his path from a Harvard postdoc to leading advanced tech operations in Afghanistan after a DARPA summer program, and later developing dark-web search tools used to combat human trafficking. They discuss the role of problem selection in innovation, humility and service, and how he stays hopeful after seeing the darkest parts of humanity.

Oct 20Tue
  1. GitHub Blog · AI & ML34

    How to continue a GitHub Copilot app session in VS Code

    This GitHub tutorial explains how to open a GitHub Copilot app session in VS Code so its conversation, diff, and worktree carry over, letting you review changes line by line, edit by hand, run tests locally, and push updates to the session's pull request. It lists prerequisites and walks through each step, including prompting Copilot further from the sidebar and committing with normal Git commands.

Oct 16Fri
  1. Microsoft Research58

    Proof-based general-purpose computing with Magnetar

    Microsoft Research introduces Magnetar, a zero-knowledge virtual machine that proves execution of RISC-V programs written in Rust, Go, or C, running 10 to 39 times faster on average than the fastest comparable zkVMs across 15 benchmarks and verifying proofs in about 12 milliseconds. Magnetar's security rests only on standard hash functions such as SHA-256 or SHA-3, requiring no elliptic curves, lattices, special-purpose hash functions, trusted setup, or unproven conjectures. It builds on more than a decade of Microsoft Research work including Spartan, Phalanx, Nova, Brakedown, Lasso, Jolt, and BinarySpartan.

Oct 14Wed
  1. GitHub Blog · AI & ML34

    GitHub’s model for secret detection: Finding more secrets with less noise

    GitHub describes a two-stage secret scanning pipeline that uses regex candidate generation followed by a fine-tuned ModernBERT classifier that reads each candidate alongside its file path, variable names, and surrounding code. Compared with its previous LLM-based pipeline, GitHub reports 11.5% higher repository owner-weighted acceptance, 86% lower median post-push scan execution time, and more than 98% lower serving cost, with offline evaluation showing 13% higher precision and 19% higher recall. The efficiency gains made it practical to extend generic secret detection to push protection.

Oct 13Tue
  1. Microsoft Research52

    Simulating student learning for better AI tutoring

    Microsoft Research introduces StudentSim, a research system that trains personalized AI student simulators from publicly available, de-identified learner records to evaluate and improve AI tutors before testing with real learners. The work also presents StudentSimEval, a benchmark spanning chess, second-language English writing, and mathematics that measures behavioral fidelity and guidance responsiveness, with StudentSim outperforming baselines such as GPT-5.4 and Maia2. In a chess tutor reinforcement learning study, a tutor trained with StudentSim feedback reached 90.5% accuracy versus 75.7% for the no-RL baseline and 71.6% for the GPT-5.4 reward condition, as rated by blind human chess experts.

Oct 8Thu
  1. GitHub Blog · AI & ML58

    Secret protection must scale with software

    GitHub reports that one in three pull requests now involves an AI agent, up from fewer than one in 10 a year ago, while a new secret appears in public code roughly every two seconds. GitHub introduces a ModernBERT-based classifier built with Microsoft Applied Sciences that extends push protection to unstructured secrets, evaluating candidate batches in under two milliseconds and potentially more than doubling the secrets it can prevent. The feature enters private preview and will be available to organizations with GitHub Secret Protection on Enterprise Cloud and GitHub Teams, and will ship with GitHub Enterprise Server 3.23 in public preview.

  2. Microsoft Research71

    Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

    Microsoft Research Asia introduces Harnessed Agentic RL, a paradigm in which the same agent harness used in deployment participates directly in reinforcement learning, and open-sources Agent Lightning v1.0, a roughly 3,500-line framework with an API Gateway, Rollout Controller, and Customized Trainer built on verl. Agents run as standard Kubernetes jobs without commercial sandbox services, and Collocated Async RL shares GPUs between rollout and updates, achieving about a 2x end-to-end speedup over synchronous RL. An end-to-end coding agent pipeline raised Qwen3.5-9B from 41.8% to 56.4% Pass@1 on SWE-bench Verified using about 6,000 training samples.

    Why it matters: It offers a concrete, reproducible recipe for training deployed agents in place, with code size, infrastructure, and benchmark numbers stated explicitly.

Oct 7Wed
  1. Microsoft Research23

    What AI gets wrong and what failure teaches us

    In a Microsoft Research Podcast conversation with Chad Atalla, Microsoft partner research manager Jennifer Neville discusses how her team uses practical evaluations—multiturn conversations, collaborative environments and long-horizon agentic tasks—to find where current AI systems break down beyond traditional benchmarks. She describes research showing that model performance degrades significantly over multiturn interactions and that errors can accumulate in long workflows, and offers practical guidance such as restarting a chat with a fully specified single turn and checking answers rather than fully delegating work.

Oct 5Mon
  1. GitHub Blog · AI & ML61

    ReviewBench: An open benchmark for AI code review

    GitHub introduces ReviewBench, an open benchmark for AI code review built from analysis of 103.9 million GitHub pull requests, containing 219 public pull requests across 19 languages with a multi-source golden set labeled for severity and category. It reports grounded and augmented precision, recall, and F1 metrics, uses Claude Sonnet 5 as the LLM grader under a published rubric, and had its ground-truth findings independently re-labeled by senior engineers with 96.6% agreement. GitHub says ReviewBench's offline predictions for a Copilot code review multi-model ensemble matched a later A/B test, which showed an 8.0% rise in addressed rate, 13.6% in recall, 61% in comment volume, and an 8.0% drop in cost per review.

    Why it matters: It offers a concrete, reproducible methodology and public dataset for comparing AI code reviewers, plus evidence on how offline scores tracked a production experiment.

Oct 1Thu
  1. Microsoft Research40

    Forecasting space weather risks on power grids

    A Microsoft Research intern developed a machine learning pipeline that uses forecast-time solar-wind data, AE and Dst index forecasts, local geology and grid data to produce location-specific geomagnetically induced current risk estimates for 66,935 substations in the continental United States, 30 to 60 minutes ahead. During the 2020-2026 evaluation period the pipeline detected nearly 80% of major space-weather events, with detection rates of 76.5% for events ≥10 nT/min, 81.2% for ≥20 nT/min and 64.1% for ≥50 nT/min, and it produced estimates for all substations in about 333 milliseconds. The authors note that further validation with utilities and operational data would be needed before use in grid operations.

Sep 26Sat
  1. GitHub Blog · AI & ML55

    GitHub Copilot app for Beginners: How to build custom workflows with canvases

    GitHub explains canvases in the GitHub Copilot app: customizable, bidirectional interfaces such as kanban boards, checklists, or dashboards that you create by entering the /create-canvas skill in an agent session and describing the workflow in plain English. The agent builds the interface in the right-side panel, and both you and the agent can update its shared state immediately without a separate sync step. Canvases are saved as extensions for reuse or team sharing, and ready-made ones are available through Awesome Copilot.