arXiv cs.AI· Hongchen Wei, Daohong Ying, Zhi Hong, Aidan Wu, Bei Liu, Ruichun Ma, Yifan Yang, Qi Dai, Kai Qiu, Rui Xu, Dongdong Chen, Zhenzhong Chen, Chong Luo·· 12 小时前AI 评分52
OfficeTown：用生成式组织仿真为智能体提供工作场景数据
OfficeTown: Generative Organizations for Evolving Agent Intelligence
AI 导读
论文提出 OfficeTown，一个面向专业组织的生成式仿真框架，可根据组织的自然语言描述初始化成员分工、委派任务并评审交付物，产物与未决事项会持续影响后续工作。
正文 · 原文
Authors:Hongchen Wei, Daohong Ying, Zhi Hong, Aidan Wu, Bei Liu, Ruichun Ma, Yifan Yang, Qi Dai, Kai Qiu, Rui Xu, Dongdong Chen, Zhenzhong Chen, Chong Luo
Abstract:AI agents increasingly use files and software tools to produce professional deliverables. Such work draws on materials, decisions, and feedback accumulated across assignments. We introduce OfficeTown, a generative simulation framework for professional organizations in which tasks and their context arise from continuing organizational work. From a natural-language description of an organization's purpose and activities, OfficeTown initializes an organization whose members coordinate work, delegate bounded assignments to assistant agents, and review their deliverables. Artifacts, decisions, and unresolved matters persist and shape subsequent work, while responsibilities and permissions determine what each member can see and do. We curate work requests, their context, and review feedback into executable resources for evaluation and learning. We construct OfficeTown Bench with 406 tasks and 4,836 evaluation criteria from 23 simulated organizations across eight professional domains, incorporating domain-expert feedback. Frontier-model evaluations reveal substantial remaining headroom. Supervised fine-tuning and on-policy distillation on OfficeTown data substantially improve Qwen3.5-9B on external professional-work benchmarks, including GDPval. Supplementary evaluations show that the principal--assistant harness achieves higher scores than the single- and multi-agent baselines tested. These results highlight the value of organizational simulation in generating work-grounded data and environments for the continued development of generalist agents.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.23194 [cs.AI] |
| (or arXiv:2610.23194v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.23194 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hongchen Wei [view email]
[v1]
Fri, 16 Oct 2026 09:32:16 UTC (6,114 KB)
来源：arXiv cs.AI · arxiv.org