Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 3·5 min read
APEx: Distillation of Agent Procedural Experience for Adaptive Deep Research Question Answering
TL;DR
APEx is a novel framework that enhances deep research question answering by distilling procedural experience from agents. It outperforms existing models, including GPT-5.4, by 14.7 points on multiple benchmarks.
✦ Why It Matters
Consider implementing APEx to enhance your AI systems' ability to learn from past experiences.
Key Takeaways
- Explore hierarchical frameworks for organizing agent experiences.
- Utilize reward-guided skill distillation in your models.
- Implement closed-loop architectures for adaptive learning.
- Test your models against benchmarks to measure improvements.
How It Works
APEx organizes agent interaction history into two levels: instance-level trajectory memories and category-level procedural skills. The Executor executes tasks, the Distiller refines skills based on past experiences, and the Planner adapts strategies in real-time using reinforcement learning.
This closed-loop system allows for continuous improvement and alignment of skills with evolving policies.