Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 7·5 min read
When History Lies: Evaluating and Improving Tool Use under Misleading Multi-Turn Histories
TL;DR
A new method, termed 'ours', enhances tool-calling agents' decision-making by addressing misleading historical data, achieving 87.0% Balanced Tool-Use Accuracy on the Qwen3-1.7B model.
✦ Why It Matters
Implement the 'ours' method to enhance your AI models' decision-making accuracy today.
Key Takeaways
- Evaluate your AI's decision-making against misleading historical data.
- Use the 'bench' framework to assess tool-calling agents.
- Implement the 'ours' method for improved accuracy.
- Consider scaling with larger teacher models for better performance.
How It Works
The 'ours' method employs a teacher-student model where a teacher trained on clean data guides a student that only sees polluted histories. This soft supervision helps the student learn to make better decisions despite misleading historical context.