💻 computer science

PAUSE: A User-Centric Benchmark for Personal AI Assistants in Unified Service Environments

This paper introduces PAUSE, a user-centric benchmark designed to evaluate personal AI assistants in realistic, stateful service environments by addressing the limitations of existing fragmented benchmarks through multi-regime evaluation and revealing that even state-of-the-art models struggle with tasks requiring persistent state reasoning and configuration awareness.

Original authors: Haoyu Chen, Xirui Shi, Yuyao Wang, Jerry Chen, Di Niu

Published 2026-07-31

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CC BY 4.0

Original authors: Haoyu Chen, Xirui Shi, Yuyao Wang, Jerry Chen, Di Niu

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you've just built a super-smart robot butler. You've taught it how to open doors, turn on lights, and even order pizza. But there's a catch: in the real world, your robot doesn't just live in a vacuum. It lives in your house, with your rules. Maybe you have a secret code to the front door, a specific time when the lights can't be turned on, or a rule that the robot can't touch the cookie jar unless you give it permission first. This is the world of "Personal AI Assistants." These aren't just chatbots that answer trivia; they are digital helpers designed to manage your life, from your health data to your shopping list. But here's the big question: Can these robots actually handle the messy, complicated reality of your life, where things change, permissions are tricky, and they have to remember what happened five minutes ago to know what to do next? Scientists have been trying to test these robots, but most of their tests are like playing a video game where the rules are simple and the world resets every time. They don't really test if the robot can handle the "real deal" of a unified, stateful environment where your personal settings and permissions matter.

Enter a new study called PAUSE, which acts like a giant, realistic obstacle course for these AI assistants. The researchers, a team from the University of Alberta, built a simulated world that feels just like a real person's digital life. In this world, the AI has to juggle different services—like checking your workout logs, managing your health appointments, or buying groceries—while respecting your specific permissions and the current state of your accounts. Think of it as a "choose your own adventure" book where the AI has to keep track of every choice you've made, every password you've set, and every subscription you have, all while trying to complete a task. The team created a pipeline to generate hundreds of these tricky scenarios, ranging from simple data lookups to complex, multi-step shopping missions that require the AI to ask you for help if it hits a permission wall.

When they put the latest and greatest AI models through this PAUSE test, the results were a bit of a reality check. Even the most advanced, expensive AI models from big tech companies struggled. While they were great at simple tasks, their success rate dropped significantly when they had to reason about complex, changing states or figure out hidden system rules. In fact, on the hardest tasks requiring this kind of "stateful reasoning," even the top models failed to complete the task more than 30% of the time, meaning they couldn't reach the 70% success mark. The study suggests that simply being good at calling tools isn't enough; the AI needs to be much better at understanding the "why" and "how" of your personal digital environment. The researchers found that the biggest stumbling block wasn't the AI's ability to talk or read, but its ability to remember and reason about the invisible rules of your life, like a robot forgetting that it needs your permission before it can open the cookie jar. This work doesn't just show us where the robots are failing; it gives us a new, fairer way to test them so we can build assistants that are truly ready to help us in the real world.

Technical Summary: PAUSE – A User-Centric Benchmark for Personal AI Assistants

Problem Statement

Personal AI assistants are increasingly deployed as tool-augmented agents operating within unified service environments to support everyday user activities. Realistic deployment requires these agents to reason over persistent user states, respect user-specific configurations (permissions, subscriptions, account settings), and sustain long-horizon, constraint-aware interactions across multiple services.

Existing benchmarks for tool-using Large Language Models (LLMs) suffer from significant limitations in this context. Early benchmarks focused on API coverage and tool invocation accuracy, while more recent efforts (e.g., MCP-based benchmarks) expanded scale by incorporating diverse tools. However, these approaches often fragment service contexts or abstract away user state. They fail to evaluate assistants as user-facing systems that must operate within a unified, user-centric service environment where access is gated by permissions and system configurations. Consequently, current benchmarks do not fully assess an agent's ability to maintain coherent state, reason about hidden configuration dependencies, or sustain user-coupled interactions over long horizons.

Methodology

The PAUSE Benchmark

PAUSE is a user-centric benchmark designed to evaluate personal AI assistants in stateful, service-integrated environments. The benchmark is instantiated in the domain of personal healthcare management, chosen for its heterogeneous data sources, external device integrations, and permission-sensitive resources.

Key System Characteristics:

Unified Environment: All services operate under a shared, persistent system configuration (permissions, wallet status, subscriptions) that is largely invisible to the assistant and must be inferred.
User-Centric Constraints: Assistants cannot modify critical system configurations directly; they must interact with the user to resolve constraints (e.g., requesting permission to connect a device or top up a wallet).
Task Diversity: The benchmark includes 180 tasks across three categories:
1. Data & Log Tracking (Easy): Direct data retrieval.
2. Data & Log Tracking (Hard): Multi-step execution requiring system configuration reasoning (e.g., connecting disconnected sources, upgrading subscriptions).
3. Shopping: Long-context multi-constraint optimization (product selection, budget management, voucher usage).

Synthesis Pipeline

PAUSE employs a scalable, multi-agent synthesis pipeline to generate coherent environments and annotated tasks:

User-Centric Task Generation: Tasks are grounded in synthetic, template-sampled data representing concrete user-owned resources. An LLM generates high-level descriptions and explicit target conditions ( $\tau$ ) based on the underlying state.
Guided Trajectory Rollouts: State-of-the-art LLMs interact with the system and a simulated user agent to collect real-execution trajectories. A rollout prompt ( $P_{rollout}$ ) acts as an oracle policy to guide behavior.
Target-Aligned Validation: A committee of LLMs filters trajectories, selects preferred paths, and aligns them against the predefined target set $\tau$ . Tasks without feasible reference trajectories are discarded.

Evaluation Framework

PAUSE adopts a multi-regime evaluation framework tailored to task characteristics:

Open-Ended Tasks (Data/Log Tracking): Evaluated using a hybrid approach.
- Target-Based: An LLM-as-judge assesses task completion against explicit targets ( $\tau$ ).
- Trajectory-Level: Deterministic metrics (Precision, Recall, F1) measure overlap between the model's tool-call sequence and the reference trajectory.
Constraint-Intensive Tasks (Shopping): Evaluated via deterministic state-based verification (e.g., product ID, quantity, budget feasibility) without relying on reference trajectories.

Key Contributions

Holistic, User-Centric Benchmark: PAUSE introduces a benchmark that evaluates assistants in unified environments with persistent user state and permission-gated resources, moving beyond workflow-centric and tool-isolated settings.
Multi-Regime Evaluation Framework: A principled strategy combining LLM-based semantic judgment and trajectory-level behavioral overlap for open-ended tasks, alongside deterministic state verification for constraint-intensive tasks.
Scalable Synthesis Pipeline: A multi-agent pipeline that generates coherent environment states, realistic interaction trajectories, and target-aligned annotations at scale, supporting benchmark extensibility.

Experimental Results

The authors evaluated a diverse set of state-of-the-art proprietary and open-source models (including GPT-5, Gemini-3, and DeepSeek-V3.2).

Performance Gaps: Even state-of-the-art proprietary models failed to reach 70% task completion on scenarios requiring stateful reasoning and configuration awareness.
Task Difficulty: Performance degraded substantially on "Hard" tasks compared to "Easy" tasks. While top-tier models achieved near-saturated performance on easy retrieval tasks, they exhibited notable drops on hard tasks requiring implicit system constraint reasoning.
Error Analysis:
- Strong Models (e.g., Gemini-3-Flash): Failures were primarily concentrated in system configuration reasoning (e.g., failing to infer that a data source was disconnected). Basic tool usage remained robust.
- Weaker Models: Struggled fundamentally with data consistency, resource navigation, and temporal reasoning, in addition to system configuration errors.
Ablation Study: Providing explicit policy guidance regarding system configurations improved performance across all models, particularly reducing system-related errors for strong models. This suggests that while stronger models can leverage explicit policies, deeper reasoning limitations persist.
Evaluation Reliability: LLM-as-judge evaluation showed high agreement (80–87%) with human annotations, validating its use as a reliable proxy for complex, open-ended agent tasks.

Significance and Claims

The paper claims that PAUSE reveals consistent and interpretable failure patterns in current AI assistants, demonstrating that effective tool calling alone is insufficient for robust performance in realistic service settings. The benchmark highlights a clear capability gap in:

Reasoning over implicit system configurations (e.g., permissions, subscriptions).
Sustaining long-horizon, user-coupled interactions where the agent must coordinate with the user to resolve state constraints.
Maintaining coherent state across heterogeneous services.

The authors position PAUSE not just as an evaluation tool, but as a foundation for future research in model analysis, distillation, and the development of agents capable of operating in unified, user-centric service environments. The work underscores the need for agents that can reason about hidden dependencies and manage state transitions, rather than merely executing tool calls.