Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·10d ago·5 min read
Kepler: Auditable World Models for ARC-AGI-3
TL;DR
Kepler is an open-source framework that creates auditable world models for evaluating agents in interactive environments. It achieved a perfect score on 25 public games while maintaining human-level action efficiency.
✦ Why It Matters
Consider implementing Kepler to improve the evaluation of your AI agents in interactive environments.
Key Takeaways
- Explore Kepler for building auditable world models.
- Utilize retrospective transition checks for validation.
- Analyze the implications of source-code leakage in evaluations.
- Consider cost-conditioned reporting for more accurate assessments.
How It Works
Kepler operates by creating executable world models that represent hypotheses about the environment. It validates these models through two main checks: retrospective transition checks, which assess the accuracy of model predictions based on past actions, and conditional prediction checks, which evaluate the model's ability to predict future states given certain conditions.
This structured approach allows for a more rigorous evaluation of agent performance.