Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 11·5 min read
FlowBank: Query-Adaptive Agentic Workflows Optimization through Precompute-and-Reuse
TL;DR
Existing workflows in AI systems often struggle with efficiency and adaptability to varying queries. FlowBank is a novel framework that optimizes agentic workflows by leveraging precomputation and reuse of query results.
✦ Why It Matters
Engineers can implement FlowBank to enhance the efficiency of AI workflows, reducing latency and resource usage.
Key Takeaways
- Utilize FlowBank for efficient workflow management in AI systems.
- Leverage DiverseFlow to cover underrepresented queries.
- Apply CuraFlow to minimize redundancy in workflow portfolios.
- Adopt adaptive query matching to optimize performance and cost.
How It Works
FlowBank operates in three stages: first, it generates a diverse set of workflows using DiverseFlow, which targets under-covered queries to ensure a broad coverage. Next, it compresses these workflows into a compact portfolio with CuraFlow, minimizing redundancy and ensuring efficient deployment.
Finally, it matches incoming queries to the most appropriate workflow based on predicted utility, optimizing the performance-cost trade-off.