Great paper on improving proactive agents. (bookmark it) Proactive agents act before you do. But how do you evaluate something that's suppos…

Great paper on improving proactive agents. (bookmark it) Proactive agents act before you do. But how do you evaluate something that's supposed to anticipate needs you haven't expressed? This work introduces PARE, a framework that models applications as finite state machines with stateful navigation and state-dependent action spaces, enabling realistic active user simulation. Building on this, PARE-Bench provides 143 diverse tasks across communication, productivity, scheduling, and lifestyle apps, testing context observation, goal inference, intervention timing, and multi-app orchestration. Why does it matter? Current benchmarks model apps as flat tool-calling APIs, missing the stateful, sequential nature of real user interaction. PARE closes this gap, giving researchers a principled way to measure whether agents can infer goals and act at the right moment. Paper: https://arxiv.org/abs/2604.00842 Learn to build effective AI agents in our academy: https://academy.dair.ai/

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Source: DAIR.AI (X) | 2026-04-25