Five-Minute Experiments for Federal Compliance: What Autonomous AI Research Teaches Us About Assessment Efficiency

The document argues that federal compliance assessment should adopt Andrej Karpathy's autoresearch framework—which constrains AI experiments through fixed time budgets, single numeric metrics, and iterative cycles—to eliminate systemic assessment failures rooted in open-ended timelines and qualitative evaluation criteria.

This paper presents a detailed examination of the challenges and solutions addressed by Fixed-Budget Compliance, providing actionable frameworks for organizations navigating the intersection of AI adoption and regulatory compliance.

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• Establishes a structured approach to fixed budget that aligns technical implementation with federal compliance requirements.
• Demonstrates how Fixed-Budget Compliance concepts translate directly into measurable engineering controls and audit artifacts.
• Provides implementation patterns validated against real-world deployment constraints in regulated environments.
• Identifies critical gaps in current approaches and proposes concrete architectural solutions backed by operational evidence.