# Reference-Grounded Test Generation

This work introduces RecreationWorld, a framework, and RecreationBench, a benchmark: given a running reference application, an agent must discover its behavior and deliver a buildable, runnable recreation, with reproducible environments on Ubuntu, macOS, Windows, Android, and Web plus a unified GUI-and-coding harness, automatic scoring by reference-derived hidden programmatic and visual assertions, and 35,000 filtered recreation trajectories for training; evaluation shows GPT-6 Astra leading at 58.1% overall while passing all programmatic tests on just 2.8% of tasks, and recreations reproducing static interface structure more reliably than interactions and computed outputs.

This work introduces RecreationWorld, a framework, and RecreationBench, a benchmark: given a running reference application, an agent must discover its behavior and deliver a buildable, runnable recreation, with reproducible environments on Ubuntu, macOS, Windows, Android, and Web plus a unified GUI-and-coding harness, automatic scoring by reference-derived hidden programmatic and visual assertions, and 35,000 filtered recreation trajectories for training; evaluation shows GPT-6 Astra leading at 58.1% overall while passing all programmatic tests on just 2.8% of tasks, and recreations reproducing static interface structure more reliably than interactions and computed outputs.