Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·May 27·5 min read
Anchor: Mitigating Artifact Drift in Agent Benchmark Generation
TL;DR
AI agent benchmarks suffer from artifact drift—misalignment between task instructions, test environments, automated verifiers, and success criteria created by separate teams. Anchor is a framework that enforces consistency across these components through unified specification and validation.
✦ Why It Matters
Engineers can use Anchor to catch task specification errors before deploying agent benchmarks, reducing wasted evaluation effort.
Key Takeaways
- Couple task instructions, environments, and verifiers to prevent specification misalignment.
- Validate benchmark components before deployment to catch unsolvable or ambiguous tasks early.
- Use unified semantic definitions across all task-creation teams to ensure consistency.
- Test verifiers against ground-truth oracles to detect reward misspecification.
Full Summary
Training and evaluating AI agents on realistic business tasks requires environments that are simultaneously realistic, verifiable (can measure success objectively), and scalable (can generate many tasks). Artifact drift occurs when loosely coupled teams create instructions, environments, oracles (ground-truth success definitions), and verifiers (automated checkers) independently, causing fundamental disagreements about what constitutes task completion.
Anchor addresses this by providing a unified framework that couples these components, ensuring they reference the same task semantics. The approach validates consistency between specifications and verifiers before benchmark deployment, catching misalignments early.
Results show Anchor reduces unsolvable tasks and reward misspecification in generated benchmarks, improving reliability for evaluating long-horizon agent behavior in enterprise workflows.