Agent Leaderboard Rankings May Reflect Scaffolds, Not Capability
A paper posted on arXiv on September 30 and updated October 5, 2026, develops a Bayesian variance-decomposition framework for measuring the reliability of sparse, imbalanced agent leaderboards, challenging the assumption that simply adding more evaluation tasks can fix ranking uncertainty arXiv.
The research applies the framework to 22 benchmarks drawn from the Holistic Agent Leaderboard and Harbor Index. The core finding: leaderboard rankings can reflect evaluation conditions—such as scaffold choices and task composition—rather than pure agent capability.
Reliability Varies Sharply by Evaluation Scope
The study reports a sharp gap between two types of reliability. Rankings of fixed model–scaffold systems (a specific agent tested under a specific evaluation setup) showed reliability between 0.935–0.994, indicating stable results. By contrast, rankings of the underlying models themselves—abstracted away from their scaffolds—showed reliability ranging only from 0.148–0.841, far more volatile arXiv.
This difference matters because it reveals what a leaderboard actually measures. A high-reliability score for a fixed setup may give false confidence that the ranking captures agent capability when, in fact, it may largely reflect the evaluation frame itself.
Adding Tasks Has Hard Limits
The paper identifies a critical constraint: when the dominant limitation is inadequate scaffold coverage—meaning the benchmarks do not represent the diversity of real-world deployment scenarios—adding infinitely many similarly constructed tasks improves model-ranking reliability by no more than 0.097 arXiv.
In other words, if your evaluation suite is narrowly designed, you cannot brute-force your way to truth by running more tests under the same constraints.
Pooling Diverse Benchmarks Shows Promise
The research identifies a more effective path: pooling benchmarks from diverse sources. When researchers combined results across different leaderboards, projected cross-task ranking reliability rose from 0.44 to 0.75 at the same task budget. This pooling strategy also reduced projected evaluation cost by up to 83%, suggesting that breadth of evaluation sources matters more than sheer volume arXiv.
The findings have direct implications for agent developers and enterprises selecting tools. A high leaderboard ranking does not guarantee that an agent will perform well in your specific use case; the scaffolds and task distribution used in evaluation may not match your deployment environment. Similarly, leaderboard maintainers face a trade-off: expanding task count alone is inefficient if the core issue is narrow benchmark design.
The paper distinguishes between evaluating fixed agent configurations—where high reliability is achievable—and evaluating the models themselves across diverse real-world conditions, where uncertainty remains substantial unless evaluation design is broadened.