What it measures
150+ real Windows OS desktop tasks across Edge, Office, File Explorer, VS Code, and Windows Settings.
Reading the measurement
The useful outcome is a verified change or deliverable. Inspect the tool interface, initial state, user simulator and policy constraints. An agent can claim completion without reaching the required state, or reach it through an action the protocol forbids.
Published evaluation
Success across 154 Windows tasks. Accessibility trees, OCR, icon and image detectors are input-setting variables; source configurations remain visible.
Where this fits in an evaluation suite
A transcript that says “done” is weaker evidence than the verified final state. Track policy violations, recoveries, and invalid attempts alongside success.
Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.
A reproducible comparison
Success across 154 Windows tasks. Accessibility trees, OCR, icon and image detectors are input-setting variables; source configurations remain visible.
Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.
PRIMARY EVIDENCE & FURTHER READING
- Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale — published result table ↗
All 12 authors
Bonatti, Rogerio, Zhao, Dan, Bonacci, Francesco, Dupont, Dillon, Abdali, Sara, Li, Yinheng, Lu, Yadong, Wagle, Justin, Koishida, Kazuhito, Bucker, Arthur, Jang, Lawrence, Hui, Zack
Reviewed 2026-10-27. Explanation and interpretation by RLTasks. Benchmark ownership remains with its original authors.