Translation in progress Some entries still include text in the original language.
Oct 12 · Monday
Another 265 papers do not have category tags yet and are not included in filtered results.
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About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
解释后验安全的权重几何脆弱性，并提出预训练持续安全共训练
Published Sep 7
Evaluated models Qwen-2.5-7B , Qwen-2.5-7B-Instruct , Llama-3-8B and others15 Abstract 论文将后验安全脆弱性归因于正交抑制，并提出通过预训练持续共训练实现具备微调抗性的安全防护。
提出 Rationale-Only 安全微调，去掉拒答声明以降低误拒
Published Sep 4
Evaluated models Llama-3.1-8B , Mistral-7B-v0.3 , Gemma-2-9B and others8 Abstract 去掉套话拒答声明、只训理由，可降低假拒答，有害查询上的服从率仍低。这是一项针对安全微调数据的结构分析：假拒答被作者追溯到回答里的套话式拒答声明，而不是只靠推理时补救。
提出 DataShield，用共识子空间过滤会削弱安全的微调数据
Published Jul 16
Evaluated models Phi3-medium-4k-it , Qwen3-4B-it , Gemma2-27B-it and others4 You have reached the end