Efficient Benchmarking of AI Agents
Summary: arXiv:2603.23749v1 Announce Type: new
Abstract: Evaluating AI agents on comprehensive benchmarks is expensive because each evaluation requires interactive rollouts with tool use and multi-step reasoning. We study whether small task subsets can preserve agent rankings at substantially lower cost. Unlike static language model benchmarks, agent evaluation is subject to scaffold-driven distribution shift, since performance depends on the framework wrapping the underlying model. Across eight benchmarks, 33 agent scaffolds, and 70+ model configurations, we find that absolute score prediction degrades under this shift, while rank-order prediction remains stable. Exploiting this asymmetry, we propose a simple optimization-free protocol: evaluate new agents only on tasks with intermediate historical pass rates (30-70%). This mid-range difficulty filter, motivated by Item Response Theory, reduces the number of evaluation tasks by 44-70% while maintaining high rank fidelity under scaffold and temporal shifts. It provides more reliable rankings than random sampling, which exhibits high variance across seeds, and outperforms greedy task selection under distribution shift. These results suggest that reliable leaderboard ranking does not require full-benchmark evaluation.
Introduction
The field of artificial intelligence (AI) is rapidly evolving, necessitating robust methods for benchmarking AI agents. Traditional evaluation methods can be resource-intensive, involving complex tasks that require extensive interactions and reasoning capabilities. This article examines a novel approach to benchmarking that aims to streamline the evaluation process while preserving the integrity of agent rankings.
Challenges in AI Benchmarking
Current methodologies in AI benchmarking face several challenges:
- High Costs: Each evaluation involves substantial computational resources and time.
- Scaffold-Driven Distribution Shift: The performance of AI agents can vary significantly depending on the evaluation framework.
- Inconsistency in Scoring: Absolute score predictions often degrade under distribution shifts, complicating the reliability of rankings.
A Novel Approach
The researchers propose an innovative solution to address these challenges. By utilizing a mid-range difficulty filter based on historical pass rates, they aim to maintain the fidelity of agent rankings while significantly reducing the number of evaluation tasks.
Key Findings
Through rigorous testing across eight benchmarks and 33 agent scaffolds, the study yielded several noteworthy findings:
- Rank-Order Stability: Rank-order prediction remained consistent, even under varying conditions.
- Task Reduction: Implementing the mid-range difficulty filter resulted in a 44-70% reduction in evaluation tasks.
- Enhanced Reliability: The proposed method outperformed random sampling and greedy task selection, providing more stable and reliable rankings.
Conclusion
The findings of this study have significant implications for the future of AI benchmarking. By demonstrating that reliable rankings can be achieved without exhaustive evaluations, the proposed method offers a cost-effective alternative that could streamline the development and assessment of AI agents. This approach not only fosters efficiency but also ensures that the integrity of agent performance rankings is preserved, paving the way for more effective AI research and development.