Seven simple steps for log analysis in AI systems
Year: 2026
Type: preprint
Abstract: AI systems produce large volumes of logs as they interact with tools and users. Analysing these logs can help understand model capabilities, propensities, and behaviours, or assess whether an evaluation worked as intended. Researchers have started developing methods for log analysis, but a standardised approach is still missing. Here we suggest a pipeline based on current best practices. We illustrate it with concrete code examples in the Inspect Scout library, provide detailed guidance on each step, and highlight common pitfalls. Our framework provides researchers with a foundation for rigorous and reproducible log analysis.
Source: arXiv (Cornell University)
Authors: Dubois, Magda, Zorer, Ekin, Hamin, Maia, Skinner, Joe, Souly, Alexandra, Wynne, Jerome, Coppock, Harry, Sato, Lucas, Kapoor, Sayash, Dev, Sunishchal, Juchems, Keno, Mai, Kimberly, Flesch, Timo, Luettgau, Lennart, Teague, Charles, Patey, Eric, Allaire, JJ, Pacchiardi, Lorenzo, Hernandez-Orallo, Jose, Ududec, Cozmin
Institutions: Princeton University, RAND Corporation, University of Cambridge
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7154302256