Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
Regimes: An Auditable, Held-Out-Gated Improvement Loop Demonstrated on LongMemEval with ActiveGraph
TL;DR
Trusting autonomous improvement loops is challenging due to unlogged failures and opaque decision-making. Regimes, built on the ActiveGraph runtime, creates an auditable improvement loop that tracks failures and promotes fixes through a structured process.
✦ Why It Matters
Engineers can implement auditable improvement loops to enhance trust and performance in AI systems.
Key Takeaways
- Adopt event-sourced logging for better failure tracking.
- Utilize the Regimes framework for structured AI improvements.
- Explore the impact of failure-regime taxonomy on performance.
- Test improvements across various tasks using a common interface.
How It Works
Regimes operates by maintaining an append-only event log that records every action taken by the AI agent. This log allows for precise replay of the agent's state, enabling diagnostics of failures and the proposal of targeted repairs.
The framework includes static checks and sandbox execution to validate proposed changes before they are promoted, ensuring that improvements are both safe and effective.