Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants

Apple ML Research · 2 days ago

Researchers introduced Pare, a framework that simulates active users to evaluate proactive AI agents in digital environments by modeling apps as finite state machines rather than flat APIs. The framework includes Pare-Bench, a benchmark with 143 tasks across communication, productivity, scheduling, and lifestyle applications that test context observation, goal inference, intervention timing, and multi-app coordination. This approach addresses limitations in existing proactive agent evaluation methods that fail to capture the stateful nature of real digital interactions.