Open Model Lab
UI Understanding Evaluation
Can screenshot-based tasks distinguish OCR success from actual UI reasoning?
Status
- Status
- planned
Status: Planned. This page is a report scaffold. It does not contain model scores, charts, or completed run results.
Research question
Can screenshot-based tasks distinguish OCR success from actual UI reasoning?
Planned setup
- Build screenshot QA and UI grounding tasks.
- Separate OCR extraction from visual reasoning requirements.
- Record hallucinated UI elements and wrong interface inferences.
Planned measurements
- Score where the grader supports a score.
- Latency and cost where the run infrastructure can measure them.
- Output-quality notes and failure-mode labels.
- Known caveats and reproducibility requirements.
- OCR success, UI grounding, and visual hallucination labels.
Planned sections
- Research question and claim boundary
- Setup, model variants, data versions, and config hashes
- Eval suite or task design
- Measurements and failure modes
- Limitations, caveats, and next decision
Expected artifacts
- multimodal_evals module.
- UI understanding benchmark.
- Screenshot-task report.
Claim boundary
This report does not claim robust computer-use ability.