$π$-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913143298457600 |
|---|---|
| author | Zhang, Haoran Xu, Luxin Wang, Zhilin Gui, Runquan Zhang, Shunkai Lei, Haodi He, Zihao He, Bingsu Qin, Chicheng Zhu, Tong Qu, Xiaoye Yang, Yang Cheng, Yu Li, Yafu |
| author_facet | Zhang, Haoran Xu, Luxin Wang, Zhilin Gui, Runquan Zhang, Shunkai Lei, Haodi He, Zihao He, Bingsu Qin, Chicheng Zhu, Tong Qu, Xiaoye Yang, Yang Cheng, Yu Li, Yafu |
| contents | The rise of personal assistant agents, e.g., OpenClaw, highlights the growing potential of large language models to support users across everyday life and work. A core challenge in these settings is proactive assistance, since users often begin with underspecified requests and leave important needs, constraints, or preferences unstated. However, existing benchmarks rarely evaluate whether agents can identify and act on such hidden intents before they are explicitly stated, especially in sustained multi-turn interactions where user needs emerge gradually. To address this gap, we introduce $π$-Bench, a benchmark for proactive assistance comprising 100 multi-turn tasks across 5 domain-specific user personas. By incorporating hidden user intents, inter-task dependencies, and cross-session continuity, $π$-Bench evaluates agents' ability to anticipate and address user needs over extended interactions, jointly measuring proactivity and task completion in long-horizon trajectories that better reflect real-world use. Experiments show (1) proactive assistance remains challenging, (2) a clear distinction between task completion and proactivity, and (3) the value of prior interaction for proactive intent resolution in later tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14678 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | $π$-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows Zhang, Haoran Xu, Luxin Wang, Zhilin Gui, Runquan Zhang, Shunkai Lei, Haodi He, Zihao He, Bingsu Qin, Chicheng Zhu, Tong Qu, Xiaoye Yang, Yang Cheng, Yu Li, Yafu Artificial Intelligence The rise of personal assistant agents, e.g., OpenClaw, highlights the growing potential of large language models to support users across everyday life and work. A core challenge in these settings is proactive assistance, since users often begin with underspecified requests and leave important needs, constraints, or preferences unstated. However, existing benchmarks rarely evaluate whether agents can identify and act on such hidden intents before they are explicitly stated, especially in sustained multi-turn interactions where user needs emerge gradually. To address this gap, we introduce $π$-Bench, a benchmark for proactive assistance comprising 100 multi-turn tasks across 5 domain-specific user personas. By incorporating hidden user intents, inter-task dependencies, and cross-session continuity, $π$-Bench evaluates agents' ability to anticipate and address user needs over extended interactions, jointly measuring proactivity and task completion in long-horizon trajectories that better reflect real-world use. Experiments show (1) proactive assistance remains challenging, (2) a clear distinction between task completion and proactivity, and (3) the value of prior interaction for proactive intent resolution in later tasks. |
| title | $π$-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.14678 |