The pattern we kept seeing: same agent, same task, different OS theme = notably different results.

Claude Sonnet 4 scores 31.9% on OSWorld and Windows Agent Arena (2 of the most relevant benchmarks for computer-use agents) — but with massive variance. An agent trained on Windows 11 light mode fails on dark mode. Works on macOS Ventura, breaks on Monterey. Works on Win11, collapses on Vista.

The root cause: training data lacks visual diversity. Current benchmarks (OSWorld, Windows Agent Arena) rely on static VM snapshots with fixed configurations. They don't capture the reality of diverse OS themes, window layouts, resolution differences, or desktop clutter.

We built cua-bench — HTML-based simulated environments that render across 10+ OS themes (macOS, Win11, WinXP, Win98, Vista, iOS, Android). Define a task once, generate thousands of visual variations.

This enables: - Oracle trajectory generation via a Playwright-like API (verified ground truth for training) - Trajectory replotting: record 1 demo → re-render across 10 OS themes = 10 training trajectories

The technical report covers our approach to trajectory generation, Android/iOS environments, cross-platform HTML snapshots, and a comparison with existing benchmarks.

We’re currently working with research labs on training data generation and benchmarks, but we’d really value input from the HN community: - What tasks or OS environments should be standardized to actually stress computer-use agents? - Legacy OSes? Weird resolutions? Broken themes? Cluttered desktops? Modal hell?

Curious what people here think are the real failure modes we should be benchmarking.