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Max Out GRPO Signal: Adaptive Trace Prefix Control for Hard Reasoning Problems
Group Relative Policy Optimization (GRPO) stalls on a model's hardest problems: when no rollout in a group succeeds, the group-relative advantages vanish and the problem contributes no gradient, wasting the frontier examples we most want to learn from. Prepending a correct prefix of a reference solution raises the success rate, making prefix length a continuous knob on difficulty. Concurrent methods set the knob once; AdaPrefix-GRPO turns it into a feedback controller: throughout training it adjusts how much of the solution each problem gets, holding its success rate near 50%, where GRPO's gra
匹配采集文档中文标题、摘要、原始来源或文档类型。Flint: A visualization language for the AI era
Short chart specifications are easy to write, but often produce uninspiring results. Flint is an open-source visualization language that offers a middle path, letting AI agents create expressive charts from compact, human-editable specifications. The post Flint: A visualization language for the AI era appeared first on Microsoft Research .
匹配采集文档中文标题、摘要、原始来源或文档类型。How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization
Rotary Position Embeddings (RoPE) provide transformers with a fixed grid of positional frequencies, yet trained models use these frequencies highly non-uniformly. We study what determines this frequency usage and propose a data-centered explanation: RoPE frequencies are selected to match the relative-distance structure of the training data. Viewing each frequency as a positional lens, we formalize a field-resolution tradeoff and show that, for a data-induced dependency profile of width $W$, the optimal frequency scales as $1/W$. This frequency-matching principle explains controlled observation
匹配采集文档中文标题、摘要、原始来源或文档类型。Build an AI-powered AWS support companion with Amazon Bedrock AgentCore
In this post, you build an AWS Support Companion using Amazon Bedrock AgentCore. The agent uses Strands Agents as the orchestration framework and connects to AWS services through the Model Context Protocol (MCP). By the end, you have a working agent that can analyze CloudWatch logs, search AWS documentation, query community knowledge from AWS re:Post, and create support cases, all from a single conversational interface. The solution deploys with a single script using AWS CloudFormation and includes a web frontend built on AWS Amplify for interacting with the agent.
匹配采集文档中文标题、摘要、原始来源或文档类型。How Open Models Are Driving AI Research
Every year, the International Conference on Machine Learning (ICML) reveals where thousands of AI researchers have decided to put their work. This year’s accepted papers reveal a clear direction: open frontier models and open AI infrastructure have become foundational to how modern AI science gets done. NVIDIA had 74 papers accepted at ICML 2026. Approximately […]
匹配采集文档中文标题、摘要、原始来源或文档类型。Manage AI applications on Mac with Jamf’s AI Governance and Amazon Bedrock
In this post, we show how you can use Jamf’s AI Governance with Amazon Bedrock to configure, deploy, and validate managed settings for AI applications across a Mac fleet.
匹配采集文档中文标题、摘要、原始来源或文档类型。Build a serverless image editing agent with Amazon Bedrock AgentCore harness
This post walks through building a serverless image editor where users upload a photo, describe an edit in plain English, and receive the result in seconds. The agent runs on AgentCore harness without custom orchestration code. We deploy the full solution, including authentication, encrypted storage, three image editing tools, and a React frontend, with a single deployment command. The infrastructure is defined using AWS Cloud Development Kit (AWS CDK).
匹配采集文档中文标题、摘要、原始来源或文档类型。Institutional Red-Teaming: Deployment Rules, Not Just Models, Causally Shape Multi-Agent AI Safety
We introduce institutional red-teaming, an evaluation methodology for testing deployment rules in multi-agent AI: hold the agents, objectives, and task state fixed, vary only one rule, and attribute the resulting change in collective behavior to that rule. We instantiate the methodology in IABench-CA, a consequence-allocation benchmark spanning 228 contexts, five canonical rules, and seven model populations (33,924 games), with a normative cooperative reference and auto-labelled reasoning traces. Three findings emerge. (1) Deployment rules causally alter collective safety: changing only the co
匹配采集文档中文标题、摘要、原始来源或文档类型。SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents
Autonomous AI agents can execute complex tasks with limited human review, yet they often lack the grounded operational knowledge to make their outputs not just executable but correct, secure, and maintainable. We introduce SkillCenter, to our knowledge the largest open skill library for agents by total count: 216,938 structured skills across 24 domain bundles. A SkillGate-filtered pipeline contributes 114,565 source-grounded skills from peer-reviewed journals, ArXiv, and over 24,000 technical sources, integrated with 102,373 community skills from GitHub and the ClawHub marketplace. We present
匹配采集文档中文标题、摘要、原始来源或文档类型。AI Innovators Adopt NVIDIA Vera — Why Max Single-Threaded CPU at Scale Matters
Max single-threaded CPUs at scale are a new category of CPUs built for the agentic AI era. Across the creation and deployment of an agentic system, the CPU is on the critical path for reasoning, response time and learning. CPUs are the processor which executes the work the AI model commands: the tool calling, code […]
匹配采集文档中文标题、摘要、原始来源或文档类型。AWS Weekly Roundup: Claude Sonnet 5 on AWS, Amazon WorkSpaces for AI agents, AWS service availability updates, and more (July 6, 2026)
A couple of editions ago I wrote about what I find so energizing about working with startups. Last week I got a fresh dose of it: I spent a few days with the AWS Startups team, listening to stories of founders talking about the problems they’re actually solving. One story that stayed with me came […]
匹配采集文档中文标题、摘要、原始来源或文档类型。The Influence of Artificial Intelligence on Recruitment and Selection Practices: Evidence from KPMG East Africa
IOSR Journal of Humanities and Social Science | article | This study examines the influence of artificial intelligence (AI) technologies on recruitment and selection processes at KPMG East Africa, focusing on their impact on efficiency, candidate experience, and ethical and regulatory considerations. Employing a case study design, data were collected from 50 human resource (HR) professionals using structured questionnaires. Descriptive statistics, Pearson correlation, and multiple linear regression analyses were conducted using SPSS. The findings indicate that AI technologies exert a strong, s
匹配采集文档中文标题、摘要、原始来源或文档类型。Monitoring discriminative ML models using Amazon SageMaker AI with MLflow
Implementing a data and model monitoring solution is necessary to maintain prediction accuracy and help achieve the best outcome for your machine learning use case. This post shows how you can use open source Evidently together with Amazon SageMaker AI to generate monitoring reports, organize and compare the results in MLflow, scale through pipelines, and trigger drift notifications.
匹配采集文档中文标题、摘要、原始来源或文档类型。How Nations Are Deploying AI for Strategic Priorities
Nations have long invested in domestic infrastructure to advance their economies, protect and use their data, and take advantage of technology opportunities in areas such as transportation, communications, commerce, entertainment and healthcare. AI, the most important technology of our time, is turbocharging innovation across every facet of society. Countries are investing in AI capabilities so […]
匹配采集文档中文标题、摘要、原始来源或文档类型。Does Bielik Know What It Doesn't Know? Activation Dispersion Separates Entity Familiarity from Factual Reliability Across Model Scale
Large language models hallucinate most about entities they have never seen. We ask whether a model's activations betray entity familiarity before a single answer token is generated, and whether that signal predicts the factual reliability of the answers. On four Polish Bielik models (1.5B-11B parameters), we probe four entity domains (athletes, cities, writers, musicians), each with 42 well-known, 42 obscure-but-real, and 42 fabricated entities addressed by a one-sentence question (504 prompts per model). Two unsupervised, single-forward-pass dispersion measures over post-SwiGLU MLP activation
匹配采集文档中文标题、摘要、原始来源或文档类型。PeTeR: Post-Training Robustification of Probabilistic Circuits
Probabilistic circuits (PCs) can model complex joint distributions while supporting exact and efficient computation of many inference queries. However, standard likelihood-based PC learning is vulnerable to overfitting and fragile generalization when confronted with data noise, small sample sizes, or distribution shifts. This can be mitigated using distributionally-robust optimization which consider worst-case distributions within a Wasserstein ball of the empirical distribution, but current methods are limited to training a model from scratch in this framework. Instead, we propose PeTeR: a no
匹配采集文档中文标题、摘要、原始来源或文档类型。Agon: Competitive Cross-Model RL with Implicit Rival Grading of Reasoning
Reinforcement learning from verifiable rewards (e.g. GRPO) is the engine behind today's reasoning models, yet it grades only the final answer. On hard problems this trains models to write more rather than to think better, since the trace itself is never graded and no label for good thinking exists. We introduce Agon, which makes two competing models each other's graders. Both attempt the same problem; in alternating roles, one drafts a solution and the other reads it while solving, and each is rewarded for out-solving the other. To win, a model must out-reason a rival that has seen its work, s
匹配采集文档中文标题、摘要、原始来源或文档类型。Hardware-Rooted AI Security That Won’t Slow You Down
AI has transformed how organizations operate, driving unprecedented levels of productivity and innovation. However, AI adoption can be impeded by concerns...
匹配采集文档中文标题、摘要、原始来源或文档类型。Meet Brain: The AI system behind Azure reliability
Learn how Microsoft is building a digital twin of Azure Service Health and why it changes how hyperscale operates. The post Meet Brain: The AI system behind Azure reliability appeared first on Microsoft Azure Blog .
匹配采集文档中文标题、摘要、原始来源或文档类型。Helping K–12 educators build practical AI skills
OpenAI Academy and the Walton Family Foundation are bringing hands-on AI Skills Jams to help K–12 educators build practical AI skills for the classroom.
匹配采集文档中文标题、摘要、原始来源或文档类型。