Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 26·5 min read
VineLM: Trie-Based Fine-Grained Control for Agentic Workflows
TL;DR
Existing workflow managers assign static plans for agentic workflows, limiting adaptability during execution. VineLM is a workflow manager that dynamically selects models for each stage based on real-time objectives.
✦ Why It Matters
Engineers can leverage VineLM to enhance the efficiency and accuracy of AI workflows in real-time applications.
Key Takeaways
- Implement VineLM for real-time model selection in workflows.
- Utilize trie structures for managing execution paths effectively.
- Adopt sparse profiling to reduce offline profiling costs.
- Aim for higher accuracy within budget constraints in AI applications.
Full Summary
Agentic workflows combine configurable stages with tool stages, often requiring retries or refinements. Traditional workflow managers create a fixed plan that does not adapt during execution, which can lead to suboptimal performance.
VineLM addresses this by using a trie data structure to represent possible execution paths, allowing for fine-grained control over model selection at each stage. It employs checkpointing and cascade profiling to estimate the accuracy, cost, and latency of different paths without exhaustive profiling.
In experiments with NL2SQL and math reasoning tasks, VineLM achieved up to 18% higher accuracy within the same budget constraints. Additionally, its sparse profiling significantly reduced the need for extensive offline profiling, making it a more efficient solution for managing complex workflows.
This adaptability can lead to better resource utilization and improved outcomes in AI-driven applications.