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

arXiv: https://arxiv.org/abs/2604.00842 · HTML: https://mlsanity.com/paper/2604.00842/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environment](https://mlsanity.com/paper/2506.07982/) (2025)
- [AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents](https://mlsanity.com/paper/2407.18901/) (2024)
- [ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities](https://mlsanity.com/paper/2408.04682/) (2024)
- [TaskBench: Benchmarking Large Language Models for Task Automation](https://mlsanity.com/paper/2311.18760/) (2023)
- [Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance](https://mlsanity.com/paper/2410.12361/) (2024)
- [AgentSynth: Scalable Task Generation for Generalist Computer-Use Agents](https://mlsanity.com/paper/2506.14205/) (2025)
- [ContextAgent: Context-Aware Proactive LLM Agents with Open-World Sensory Perceptions](https://mlsanity.com/paper/2505.14668/) (2025)

## Built on by (most cited first, 15 of 15)

- [KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation](https://mlsanity.com/paper/2604.08455/) (2026)
- [π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows](https://mlsanity.com/paper/2605.14678/) (2026)
- [ProactBench: Beyond What The User Asked For](https://mlsanity.com/paper/2605.09228/) (2026)
- [Audio Interaction Model](https://mlsanity.com/paper/2606.05121/) (2026)
- [Agentic Coding Needs Proactivity, Not Just Autonomy](https://mlsanity.com/paper/2605.06717/) (2026)
- [SentinelBench: A Benchmark for Long-Running Monitoring Agents](https://mlsanity.com/paper/2606.05342/) (2026)
- [Ask Now, Use Later: Benchmarking the Proactivity Gap in Long-Lived LLM Agents](https://mlsanity.com/paper/2605.28108/) (2026)
- [Towards Better Agents for Multi-Turn User Interaction: The Next User Turn Is More Than Context](https://mlsanity.com/paper/2608.17499/) (2026)
- [Disclosure-Gated User Simulation for Companion-Agent Evaluation](https://mlsanity.com/paper/2609.00982/) (2026)
- [Perceive Before Reasoning: A Pre-Reasoning Perception Framework for Efficient and Reliable Proactive Mobile Agents](https://mlsanity.com/paper/2606.03236/) (2026)
- [Searching for Synergy in Shared Workspace Human-AI Collaboration](https://mlsanity.com/paper/2606.18413/) (2026)
- [Relevance Does Not Imply Applicability: Experience Activation for Personal GUI Agents](https://mlsanity.com/paper/2609.33304/) (2026)
- [Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agents](https://mlsanity.com/paper/2609.37236/) (2026)
- [Foundations of Proactive Agents: Principles, Technical Layers, and Proactivity-Gym](https://mlsanity.com/paper/2609.37267/) (2026)
- [Who Is Your Agent Serving? Provider-Side Indirect Prompt Injection in Proactive Agents](https://mlsanity.com/paper/2610.05266/) (2026)

## Abstract (arXiv)

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.