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Computer Use at the Edge of the Statistical Precipice — Pierluca D'Oro, Programma Labs
Vary task data, appearance, and initial state so agent evals measure adaptation instead of rewarding replayed action scripts.
AI EngineerAI EngineerAI EngineerAug 14, 20262 min
Source Summary
Static computer-use benchmarks can reward memorized action scripts rather than adaptation. Vary task state, verify every generated case, and calculate uncertainty across both actions and environments.
Practical Implication
For agent evals, vary data, appearance, and initial state; automatically reject invalid combinations; and use privileged verifiers inside a sandbox. DGWorld applies this design across 15 apps, 387 scenarios, and 3.2 million verified configurations.
Agent-Ready Context
A replay agent stores one winning trajectory per task and blindly repeats it. On deterministic OSWorld and MobileWorld-style evaluations, a script **under 1 MB** can match or beat the frontier model that generated its traces, exposing benchmark replayability. For agent evals, vary data, appearance, and initial state; automatically reject invalid combinations; and use privileged verifiers inside a sandbox. DGWorld applies this design across **15 apps, 387 scenarios, and 3.2 million verified configurations**. Uncertainty must cover both model actions and environment variation. The talk reports that rollout-only intervals can provide roughly **17–20% coverage** where a correctly structured method approaches the intended 95%, but applying that method requires a benchmark with explicit variation structure.
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