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Received β€” 21 August 2026 ⏭ NVIDIA Technical Blog

NVIDIA AVO Reaches 100% on ARC-AGI-3, Demonstrating a Frontier-Level General-Purpose Architecture for Long-Horizon Autonomous Agents

21 August 2026 at 13:00
A frontier language model is only one component of an AI agent. The surrounding agent systemβ€”often called a harnessβ€”determines how the model receives...

A frontier language model is only one component of an AI agent. The surrounding agent systemβ€”often called a harnessβ€”determines how the model receives context, uses tools, maintains state, responds to feedback, recovers from failure, and sustains progress over long-running tasks. The challenge is how to build the agent architecture that makes frontier language models work reliably on extended…

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Where Security Fits in an AI Agent Stack

21 August 2026 at 13:00
As AI agents become more capable and operate over longer horizons, building security and trust into the applications they power becomes increasingly important....

As AI agents become more capable and operate over longer horizons, building security and trust into the applications they power becomes increasingly important. Drawing on work with NVIDIA OpenShell, agent developers, open-source projects, and partners across the ecosystem, AI safety and security teams at NVIDIA offer their perspective on the emerging agent stackβ€”including the role of each layer…

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Received β€” 14 July 2026 ⏭ NVIDIA Technical Blog

How to Run an Autoresearch Workflow with RL Agent Skills and NVIDIA NeMo

14 July 2026 at 16:00
Coding AI agents are becoming practical operators for long-running machine learning (ML) workflows. They can inspect repositories, set up runtimes, resolve...

Coding AI agents are becoming practical operators for long-running machine learning (ML) workflows. They can inspect repositories, set up runtimes, resolve build issues, launch experiments, monitor execution, analyze metrics, and summarize results. For reinforcement learning (RL) research, this matters because meaningful metrics often appear only after the essential experiment infrastructure…

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Received β€” 15 June 2026 ⏭ NVIDIA Technical Blog

Pretrained to Imagine, Fine-Tuned to Act: The Rise of World-Action Models

15 June 2026 at 12:00
Quick glossary for readers new to VLA/WAM terminology VLA Vision-Language-Action model: a robot policy that starts from a pretrained VLM backbone and adapts it...

Quick glossary for readers new to VLA/WAM terminology VLA Vision-Language-Action model: a robot policy that starts from a pretrained VLM backbone and adapts it to generate actions from visual observations and language instructions. Large-scale VLM pretraining is a core part of the recipe. See Pi-0 and GR00T N1. WAM World-Action Model: a policy that starts from a pretrained world-model or video…

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Received β€” 9 June 2026 ⏭ NVIDIA Technical Blog

Accelerating Federated Learning Research with AI Agents and NVIDIA FLARE Auto-FL

9 June 2026 at 16:35
Federated learning (FL) research often begins with a deceptively simple question: What should we try next? A new aggregation rule, a FedProx coefficient, a...

Federated learning (FL) research often begins with a deceptively simple question: What should we try next? A new aggregation rule, a FedProx coefficient, a server optimizer setting, a SCAFFOLD variant, or a model architecture tweak may all look promising before an experiment starts. After the run finishes, the harder questions begin: Did the change actually improve the metric?

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