Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants
arXiv:2604.00842v1 Announce Type: new Abstract: Proactive agents that anticipate user needs and autonomously execute tasks hold great promise as digital assistants, yet the lack of realistic user simulation frameworks hinders their development. Existing approaches model apps as flat tool-calling APIs, failing to capture the stateful and sequential nature of user interaction in digital environments and making realistic user simulation infeasible. We introduce Proactive Agent Research Environment (Pare), a framework for building and evaluating proactive agents in digital environments. Pare models applications as finite state machines with stateful navigation and state-dependent action space for the user simulator, enabling active user simulation. Building on this foundation, we present Pare-Bench, a benchmark of 143 diverse tasks spanning communication, productivity, scheduling, and lifestyle apps, designed to test context observation, goal inference, intervention timing, and multi-app
arXiv:2604.00842v1 Announce Type: new Abstract: Proactive agents that anticipate user needs and autonomously execute tasks hold great promise as digital assistants, yet the lack of realistic user simulation frameworks hinders their development. Existing approaches model apps as flat tool-calling APIs, failing to capture the stateful and sequential nature of user interaction in digital environments and making realistic user simulation infeasible. We introduce Proactive Agent Research Environment (Pare), a framework for building and evaluating proactive agents in digital environments. Pare models applications as finite state machines with stateful navigation and state-dependent action space for the user simulator, enabling active user simulation. Building on this foundation, we present Pare-Bench, a benchmark of 143 diverse tasks spanning communication, productivity, scheduling, and lifestyle apps, designed to test context observation, goal inference, intervention timing, and multi-app orchestration.
Executive Summary
This article introduces Proactive Agent Research Environment (Pare), a novel framework for simulating active users in digital environments to evaluate proactive assistants. Pare addresses the limitations of existing approaches by modeling applications as finite state machines, enabling stateful navigation and state-dependent action spaces for user simulation. The authors also present Pare-Bench, a comprehensive benchmark comprising 143 diverse tasks to test proactive agents' capabilities. The framework and benchmark are designed to facilitate the development and evaluation of proactive agents, which hold significant promise as digital assistants. The study's findings have the potential to significantly advance the field of proactive AI research, particularly in the areas of context observation, goal inference, intervention timing, and multi-app orchestration.
Key Points
- ▸ Pare introduces a novel framework for simulating active users in digital environments
- ▸ Pare models applications as finite state machines for stateful navigation and state-dependent action spaces
- ▸ Pare-Bench is a comprehensive benchmark for testing proactive agents' capabilities in various digital environments
Merits
Strength in Realism
Pare's ability to model applications as finite state machines provides a more realistic representation of user interaction, enabling more accurate simulations of active users.
Comprehensive Benchmark
Pare-Bench offers a diverse range of tasks and scenarios, providing a robust evaluation framework for proactive agents and facilitating their development and improvement.
Demerits
Limited Scope
The study's focus on digital environments and proactive assistants may limit its generalizability to other domains or types of AI systems.
High Computational Complexity
Simulating active users in complex digital environments may require significant computational resources, potentially hindering the practical application of Pare in resource-constrained settings.
Expert Commentary
The introduction of Pare and Pare-Bench represents a significant advancement in the field of proactive AI research. By providing a more realistic framework for simulating active users and a comprehensive benchmark for evaluating proactive agents, the study has the potential to accelerate the development and deployment of these systems. However, further research is needed to address the limitations of Pare, such as its computational complexity and scope, and to explore its applications in other domains. Additionally, the study's findings should be carefully considered in policy discussions around the development and regulation of proactive AI systems, particularly in areas such as data protection and user consent.
Recommendations
- ✓ Future research should focus on exploring the applications of Pare in other domains and types of AI systems, such as robotics and autonomous vehicles.
- ✓ The development of Pare-based evaluation frameworks for proactive assistants should be prioritized to facilitate their deployment in real-world settings.
Sources
Original: arXiv - cs.AI
