Log Analysis Key to Credible AI Agent Evaluation
A new paper argues that current AI agent benchmarks, which report only final pass/fail outcomes, undermine evaluation credibility. The authors identify three validity threats: score inflation or deflation from shortcuts and artifacts, poor prediction of real-world utility due to scaffold limitations, and concealment of dangerous agent actions. They propose log analysis—systematic tracking of inputs, execution, and outputs—as necessary to address these issues. The paper presents a taxonomy of threats and guiding principles for log analysis, illustrated on tau-Bench Airline, where pass^5 performance was under-elicited by nearly 50%.
Key facts
- arXiv:2605.08545v1
- Agent benchmarks typically report only final outcomes: pass or fail.
- Three threats to credibility: score misrepresentation, poor real-world prediction, concealment of dangerous actions.
- Log analysis involves tracking inputs, execution, and outputs of an AI agent.
- The paper presents a taxonomy of threats and guiding principles for log analysis.
- Illustration on tau-Bench Airline shows pass^5 performance under-elicited by nearly 50%.
Entities
Institutions
- arXiv