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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.
论证发布时互信息不能认证开源权重抗微调恢复
Published Oct 7
Evaluated models EleutherAI/deep-ignorance-e2e-strong-filter , EleutherAI/deep-ignorance-unfiltered , Google BERT-Tiny and others6 Abstract 作者称零信息仍可一步恢复，表观抗微调可来自参数化。作者研究开源权重模型的篡改抵抗：发布时的互信息是否足以保证微调恢复很慢。
遮挡审计 AdvWave-P 音频越狱的频带必要性与解码层关联
Published Oct 4
Evaluated models Qwen2-Audio-7B-Instruct , LLaMA-Omni Abstract AdvWave-P 的八频带排序与能量相关。这是对加性音频越狱 AdvWave-P 的频率与解码器深度审计，受害模型主要是 Qwen2-Audio-7B-Instruct，不提出新攻击。
提出 SALVE，用文本优化检测蒸馏数据中的潜意识学习效应
Published Sep 15
Evaluated models Qwen2.5-7B-Instruct , OLMo-3-7B-Instruct , Llama-3.1-8B-Instruct and others7 Abstract SALVE 用软提示加 beam search 口头化，把潜意识数据里的教师特质恢复成可读提示。SALVE 是一种文本优化方法，用来在训练学生之前，把潜意识学习数据里读不出来的教师特质说成可读系统提示。
比较越狱微调与权重正交化去对齐对有害协助和有用性的影响
Published Apr 3
Evaluated models Qwen3-4B-Instruct-2507 , Llama-3.1-8B-Instruct , Qwen2.5-14B and others6 量化微调对仅输入侧 PII 的非预期记忆并比较隐私缓解
Published Jan 24
Evaluated models Llama 3.2 1B , Llama 3.2 3B , Llama 3.1 8B and others7 You have reached the end