Good Benchmarks

2026-07-13

AI Summary

This work addresses critical shortcomings in current AI benchmark tasks—such as insufficient correctness, ambiguous solvability, verification difficulties, unclear objectives, and unrealistic difficulty levels—by formally introducing, for the first time, five core attributes of a “good task.” Integrating task design theory with software engineering practices, the authors develop a qualitative analytical framework that emphasizes grounding tasks in real-world scenarios, articulating them in practitioners’ language, and validating outcomes based on results rather than methods. The resulting normative guidelines embed practitioner perspectives directly into benchmark design, substantially enhancing the validity and real-world relevance of evaluation tasks. This contribution provides both theoretical foundations and practical standards for developing high-quality AI benchmarks.

Abstract

Good tasks are correct, solvable, verifiable, well-specified, and hard for interesting reasons. The best tasks describe a real problem an experienced practitioner would recognize, in language a practitioner would use, with tests that verify the outcome rather than the approach.

Problem

• benchmarks
• tasks
• verification
• problem specification
• practitioner relevance

Innovation

• benchmarks
• task design
• verifiability
• practitioner-oriented
• outcome-based evaluation