Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·6d ago·5 min read
Fusion is the New Mutation: Bandit-Guided Evolution on Workflow Graphs
TL;DR
DAGO (Directed Acyclic Graph Optimization) is a new framework that optimizes workflow design by intelligently fusing parent workflows using a contextual-bandit approach. It outperforms existing methods, achieving a macro-average score of 81.7 while reducing search costs by 11.2%.
✦ Why It Matters
Consider implementing DAGO for your workflow optimization tasks to enhance performance and reduce costs.
Key Takeaways
- Explore DAGO for efficient workflow optimization in your projects.
- Utilize contextual-bandit approaches to balance exploration and exploitation.
- Leverage pretrained embeddings for better workflow fusion outcomes.
- Reduce evaluation costs while improving performance metrics.
How It Works
DAGO employs a contextual-bandit framework where each candidate workflow fusion is treated as an arm. The diagonal LinUCB policy learns a shared reward model, allowing it to predict the quality of offspring workflows.
This model balances the selection of high-potential fusions with the need to explore new combinations, optimizing the evaluation budget effectively.