Fine-grained control of language model behaviors (e.g., steering) is among the more actionable outcomes of interpretability research. For binary concepts such as refusal, a single direction in activation space often suffices for steering. However, many concepts are not binary: Animals and Countries contain many subcategories, each with multiple instances. For these concepts, the search space over

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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/2609.13072v1)获取最准确的信息。

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🔗 **原文链接**: [MAxBench: A Multinomial Concept Recovery Benchmark](https://arxiv.org/abs/2609.13072v1)
🏷️ **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: Divya Appapogu, Freya Behrens, Yonatan Belinkov, Aaron Mueller

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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。
Benchmark看多了容易麻木——跑分好不一定产品好用。这篇文章好在对分差有分析,不只是贴数据。

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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⏱️ 2026-09-15 08:02