SenWorld: A Digital-Twin Simulation for Generating Context-Rich Evaluation Data
Smartphone personal assistants reason over longitudinal personal data, yet evaluating them requires context-rich evaluation data whose correct answers are known, and real device traces are too privacy-sensitive to share. To address this challenge, we present SenWorld, a physically grounded, deterministic, event-sourced digital-twin simulation that generates such data with ground truth fixed by construction. In SenWorld, personas live through a full day in a world built from real map, weather, ho
Researchers present SenWorld, a digital-twin simulation generating context-rich evaluation data with known correct answers. The method is evaluated with 16 personas in Beijing, producing data that closely matches real-user benchmarks. SenWorld exposes 78 failures in a production smartphone assistant, confirming errors without involving large language models.
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