Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
Brick: Spatial Capability Routing for the Mixture-of-Models (MoM) Paradigm
TL;DR
Defining query difficulty in AI model deployment is challenging, as existing routers often overlook important variance within domains. Brick is a multimodal router that evaluates models based on six capability dimensions and query difficulty, optimizing for cost and quality.
✦ Why It Matters
Engineers can leverage Brick to optimize AI model deployment, balancing performance and cost effectively.
Key Takeaways
- Utilize Brick for efficient model routing in AI applications.
- Score models on multiple capability dimensions for better performance.
- Adjust the cost-quality profile based on deployment needs.
- Achieve significant cost savings while maintaining high accuracy.
How It Works
Brick evaluates models based on six capability dimensions, which include aspects like accuracy and efficiency. It combines this scoring with an estimate of query difficulty to determine the best model for each request.
The routing decision is made using a cost-penalized geometric rule, allowing for flexible adjustments between maximizing quality and minimizing costs.