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CoreWeave AIRA: The autoresearch loop for continuous improvement
Weights & Biases30 June 2026Watch on YouTube
Part of series
Ep. 3 · CoreWeave ARIA: AI Onderzoeksagent
Introductie van CoreWeave ARIA, een autonome AI-agent voor continu machine learning onderzoek via Weights & Biases.
View the seriesDescription
Meet CoreWeave ARIA, the AI Research and Iteration Agent that continuously improves your model and agent. In this demo, you will see how ARIA runs the full autoresearch loop. By continuously forming hypotheses, running experiments, evaluating results, and executing the best next actions, AIRA creates an autonomous research loop that helps models and agents improve while you focus on the problems that require human judgment. Get started: https://docs.wandb.ai/aria/overview
What you'll learn
- ARIA runs fully autonomous research loops by forming hypotheses, conducting experiments, and evaluating results without human intervention
- The system continuously improves models and agents by determining and executing the best next experiment automatically
- You can focus on complex problems requiring human judgment while ARIA handles iterative optimization automatically
Frequently asked questions
How does ARIA's autoresearch loop work?
ARIA executes a closed loop where it forms hypotheses, runs experiments, evaluates results, and determines the best next experiment based on findings. This process repeats autonomously without human intervention.
What advantages does ARIA offer for model development?
ARIA lets you focus on strategic issues while the system continuously runs experiments and optimizes models. This saves time and ensures continuous improvements.
Can ARIA improve both models and agents?
Yes, ARIA is designed to continuously improve both AI models and autonomous agents through the same research loop.