Computer Use at the Edge of the Statistical Precipice

TL;DR

This paper highlights methodological issues in evaluating Computer Use Agents and proposes principled environment design and evaluation frameworks, including a new benchmark, DigiWorld, for more reliable assessment.

Contribution

It introduces PRISM principles for environment design, develops a hierarchical bootstrap evaluation framework, and presents DigiWorld, a benchmark for realistic CUA evaluation.

Findings

Blind replay scripts can outperform static benchmarks.

PRISM principles improve environment design for CUAs.

Hierarchical bootstrap provides accurate confidence intervals.

Abstract

Evaluating Computer Use Agents (CUAs) on interactive environments is fraught with methodological pitfalls that the field has yet to systematically address. We show that a 1MB replay script that blindly executes a recorded action sequence without ever observing the screen outperforms frontier models on prominent static benchmarks, and prove that its expected success rate is exactly equal to the source agent's pass@k in deterministic environments. We trace this and other failures to two root causes: non-principled environment design (static, unsandboxed, or unreliably verified environments) and non-principled evaluation methodology (naive aggregation and misuse of pass@k for stateful UI interactions). To address the first, we propose PRISM, five design principles for CUA environments (privileged verification, realistic environments, integrity-checked configurations, sandboxed execution, and multifactorial variability) and instantiate them in DigiWorld, a benchmark of 15 realistic sandboxed mobile applications able to evaluate agents in over 3.2 million verified unique configurations. To address the second, we develop an aggregation framework pairing Wilson score intervals with hierarchical bootstrap, producing confidence intervals that correctly account for the nested structure of CUA benchmarks, as we empirically demonstrate. All together, we show that principled environment design and rigorous evaluation methodology are not optional refinements but prerequisites for meaningful CUA research.