OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution
AI agents operate in persistent environments where early state changes can influence decisions far into the future. Unlike conventional language-model interactions, agent behavior is mediated through a shared state that is repeatedly modified and reused across long-horizon workflows. Current safety benchmarks often fail to capture these cumulative risks because they focus on short, static tasks. To address these limitations, we introduce OpenART, an open-ended arena for scalable agent red teaming through environment evolution. OpenART provides over 10,000 validated stateful scenarios across 50 domains, drawing from a pool of more than 500,000 tools and skills. These tasks require a median...
| Authors | Yunhao Chen, Xin Wang, Yixu Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 August 2026 |
| Last updated at source | 29 September 2026 |
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- dongdongunique/openart-toolsDataset
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