"LLM Agent Performance" Is Not a Single Evaluation Target
Pengyu Zhu, Li Sun, Philip S. Yu, Sen Su
Why It Matters
What makes this one worth your time
Understanding the nuances of LLM agent performance evaluations can lead to more accurate comparisons and better-informed decisions in AI development.
The paper clarifies that 'LLM agent performance' is not a singular evaluation metric.
Summary
The paper argues that 'LLM agent performance' encompasses multiple evaluation targets influenced by various factors beyond the model itself, advocating for a clearer distinction in reporting and benchmarking.
Key contributions
- Proposes a framework for distinguishing between different types of LLM agent evaluations.
- Discusses implications for leaderboards and benchmark versioning to enhance fair comparisons.
Notable insights
- The distinction between model comparisons and complete agent system evaluations is crucial for accurate benchmarking.
- Robustness analysis is essential for understanding the stability of performance across varying conditions.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2602.03238v3 Announce Type: replace Abstract: LLM agent benchmark scores are shaped not only by the model but also by the agent harness, environment, evaluator, and inference budget. Unified execution controls these non-model factors by evaluating candidate models under the same configuration, making observed differences more attributable to the models themselves. However, model comparison is only one use of agent benchmarks. Other evaluations compare complete agent systems or test whether a fixed model or system remains stable across predeclared changes in its operating conditions. These results can all be reported under the common label of "LLM agent performance." Our position is that "LLM agent performance" does not denote a single evaluation target. Model comparisons under a reference stack and comparisons of complete agent systems answer different questions, while robustness asks whether either conclusion persists across predeclared conditions. The claim supported by a score therefore depends on the declared candidate boundary and condition policy. We derive implications for leaderboards, result reporting, and benchmark versioning, showing how distinguishing these classes preserves fair comparison while accommodating system innovation and robustness analysis.