Study warns that adding tasks does not always make agent rankings more reliable

A reliable ranking depends on what you intend to compare

A place in an artificial intelligence agent leaderboard seems to answer a simple question: which system performs best? But the answer can change depending on what counts as the “system.” A deployed agent combines a model with a scaffold—the software and configuration around it—and is tested on a set of tasks. The observed result belongs to that combination under those conditions, not necessarily to the model in isolation.

A study published as an arXiv preprint examines this distinction across 22 benchmarks from the Holistic Agent Leaderboard and Harbor Index. Its authors argue that the reliability of an evaluation depends on the claim it is meant to support. A ranking may consistently compare specific systems, each with a fixed model and scaffold, while still providing less certainty about which underlying model is better once the configuration is abstracted away.

This distinction matters when interpreting headlines and making purchasing or deployment decisions. If the goal is to choose between two products as they are offered, the performance of each complete configuration may be the relevant comparison. If the goal is to conclude that one model outperforms another regardless of the tools wrapped around it, evidence is needed that can separate those effects. A ranking alone does not necessarily answer both questions.

The study’s abstract reports reliability of 0.935 to 0.994 for rankings of systems with fixed models and scaffolds. For rankings of underlying models, the reported range is substantially broader and has a much lower lower bound: 0.148 to 0.841. These are ranges of results reported by the paper, not a guarantee that every benchmark or particular comparison has those values.

What the analysis separates: models, scaffolds, and tasks

The paper proposes a Bayesian variance-decomposition framework for agent leaderboards with sparse or imbalanced data. According to the abstract, the method distinguishes signal—performance differences relevant to the claim being assessed—from noise: variation that does not address that claim but can still change the ordering of systems.

In practical terms, scores can vary because models perform differently, because a scaffold helps or hinders each model to a different degree, or because the selected tasks do not adequately represent the domain about which a conclusion is intended. The abstract specifically notes that scaffold choice can change conclusions, and that the extent to which model rankings are preserved across scaffolds varies substantially from one evaluation to another.

This does not mean that scaffolding is irrelevant, or that there is a single “true” ordering of models for every use. It means the question must be defined. To evaluate an agent delivered with a particular tool, the scaffold is part of the object being evaluated. To compare capabilities attributable to the model, differences in scaffolding become a source of variation that needs to be measured or controlled.

The available abstract does not give the equations, the assumptions of the Bayesian model, the exact units for all coefficients, or the uncertainty intervals associated with each estimate. The reported ranges should therefore be read as results summarized by the authors, rather than as a complete reconstruction of the statistical analysis.

Why more tasks do not always resolve uncertainty

An intuitive response to an unstable evaluation is to add more tasks. That can help when uncertainty comes from having sampled too few tasks. However, the study warns that expanding a set with more tasks constructed in a similar way does not remove every source of variation. If the dominant problem is limited scaffold coverage, multiplying tasks under similar conditions leaves that problem largely untouched.

In the abstract, the authors estimate that even an unlimited number of similarly constructed tasks would improve a benchmark’s model-ranking reliability by at most 0.097 when uncertainty is dominated by limited scaffold coverage. This is a projection conditional on that diagnosis and on the task construction described by the paper; it is not a universal rule limiting every evaluation to that amount of improvement.

The analysis also reports an alternative: combine diverse benchmarks to compare systems across varied agentic tasks. In the projection described, pooling benchmarks raises the reliability of rankings across tasks from 0.44 to 0.75 at the same task budget and can reduce projected cost by up to 83%. These values describe projections reported by the study; they do not ensure that any combination of benchmarks will reproduce the improvement.

The practical implication is that evaluation budgets should target the relevant source of uncertainty. If representative tasks are missing, adding tasks may help. If scaffold variety is missing, more configurations need to be tested. If the goal is to generalize to a broad family of problems, combining diverse evaluations may be more informative than repeatedly extending a single benchmark.

What is known about the benchmarks—and what remains to be checked

The study says it applies its framework to 22 benchmarks from the Holistic Agent Leaderboard and Harbor Index. The available abstract does not list all 22 by name or set out detailed inclusion, exclusion, or weighting criteria. Nor does it allow us to reconstruct the distribution of tasks and scaffolds in each benchmark here. This lack of detail in the material consulted limits the ability to judge how representative the set is.

The paper does characterize the problem as involving sparse and imbalanced data, and presents its framework as a way to estimate reliability in those conditions. But it would not be appropriate to infer from the abstract alone that all benchmarks have the same composition, that imbalance was addressed in a particular way, or that their results are directly comparable without qualification.

The information provided also does not confirm whether the authors published enough data, code, and analysis to reproduce the estimates. The preprint and abstract describe the main results, but the materials available for this article do not verify whether such resources are available or what they include. A full assessment would require consulting the complete paper, its appendices, and any reproduction materials, if available.

Which conclusions are prudent, and which need more evidence

The central conclusion that can be drawn from the abstract is methodological: a ranking should be interpreted in light of its measurement goal. Reliability in comparing configured products does not automatically demonstrate that the effect of their models has been isolated. Likewise, an aggregate result across tasks does not guarantee that the ordering will hold for a particular task.

The reported results also support two specific cautions: scaffolds can change conclusions, and increasing the number of tasks does not always resolve the dominant uncertainty. The proposal to combine diverse benchmarks deserves consideration as a potentially more efficient use of evaluation budgets, but the reported improvements in reliability and cost are presented as projections from the study, not as a promise that applies without checking the conditions of each case.

Before generalizing, it would be necessary to review the full paper and verify its statistical assumptions, the precise definition of reliability, the included benchmarks, model and scaffold coverage, and cost calculations. It would also be useful to reproduce the results on other evaluation sets and check whether the conclusions hold when task and configuration selections change. Without those steps, the strongest justified reading is limited: evaluation design can influence rankings, and the claim being made determines what evidence is needed.

For readers following new evaluations, the useful question is not only who occupies first place. It also matters what was held constant, what was varied, which population of tasks the benchmark represents, and what uncertainty accompanies the ordering. These criteria help readers compare rankings carefully without confusing the performance of a deployed system with an isolated measurement of its model.

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