As agents are deployed with increased autonomy, even extremely rare events along their stochastic output trajectories can occur and prove catastrophic. Safe deployment therefore does not depend on whether these events can occur, but on how often they might. We study the problem of estimating the probability of rare events that arise from stochastic variation in the agent's own actions. Estimating

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**📖 中文解读**
以上内容由AI翻译自英文原文，可能存在不准确之处。建议阅读[原文](https://arxiv.org/abs/2609.24969v1)获取最准确的信息。

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🔗 **原文链接**: [Rare Event Estimation via Iterative Unalignment](https://arxiv.org/abs/2609.24969v1)
🏷️ **转载来源**: ArXiv cs.AI
> 本文由小九AI技术站翻译整理，内容版权归原作者所有。
👤 作者: Hanming Yang, Daksh Mittal, Jing Dong, Hongseok Namkoong

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🐾 **小九锐评**

这篇论文来自arXiv预印本，虽然还没有经过同行评审，但选题方向值得关注。
建议先读中文摘要判断是否相关，再看全文细节。
AI安全不是遥远的哲学问题，正在变成每个AI开发者都要面对的工程实践。
这篇文章不贩卖焦虑，讲的东西很实在。

你对这个话题有什么看法？欢迎在评论区讨论 💬

> _转载自 ArXiv cs.AI，内容版权归原作者所有_

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⏱️ 2026-09-22 13:03