跳到正文
arXiv:cs.AI· Jianfeng Lin, Runze Wang, Haorong Hong, Yuxuan Xie, Zeno Zeng, Zhongyuan Liu·· 16 小时前

ASIWorld:在持久生存沙盒中评测自主智能体

ASIWorld: Benchmarking Autonomous Agents in a Persistent Survival Sandbox

AI 导读

研究者提出 ASIWorld 生存沙盒,用持久世界与角色状态、角色范围观测和可编程异步接口评测自主智能体的持续目标执行。在九种智能体配置的探索性研究中,八种完成生产任务,六种完成两项任务;执行轨迹分析显示任务结果相近的配置在准备、战斗与恢复行为上存在差异,准备不充分与进展停滞相关。

正文

View PDF HTML (experimental)

Abstract:Evaluating autonomous agents requires examining whether they can translate goals into coherent action across extended interactions. In persistent environments, this requires coordinating interdependent actions under incomplete observations, accounting for delayed effects, and adapting as earlier decisions shape subsequent opportunities. We introduce ASIWorld, a survival sandbox that combines persistent world and character state with character-scoped observations and a programmable asynchronous interface to study sustained goal execution. Agents pursue interdependent production and exploration tasks through direct actions or agent-authored programs that continue running between model calls. The evaluation assesses task success, the attainment of development achievements, and survival costs, complemented by an analysis of execution behavior. In an exploratory study of nine agent configurations using a common harness, eight complete the production task and six complete both tasks. Analyses of execution trajectories and performance across difficulty levels reveal different preparation, combat, and recovery behaviors among configurations with similar task outcomes. Incomplete preparation is associated with stalled progression, while configurations that complete the tasks differ in survival costs and the fulfillment of additional achievement criteria. These findings illustrate the value of assessing sustained goal execution through both task outcomes and the behaviors and costs associated with achieving them.
Comments: 15 pages, 5 figures, 9 tables. Project website: this https URL
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.14807 [cs.AI]
  (or arXiv:2610.14807v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2610.14807

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Runze Wang [view email]
[v1] Sat, 10 Oct 2026 10:59:36 UTC (5,558 KB)

来源:arXiv:cs.AI · arxiv.org