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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.
Published Sep 28
Evaluated models E-Branchformer Abstract 梯度上升类方法更接近简单评测下的重训参照,复杂方法会被知情攻击认出,多说话人更差。
用 Dmax 与尾部损失评测后门修复的类别性能保持
Published Sep 22
Evaluated models ResNet18 , PreActResNet18 , VGG19-BN 用代价曲线评估恶意微调防御在持续训练下的衰减
Published May 14
Evaluated models Llama-2-7B-chat , Qwen3-8B , Llama-3.1-8B-Instruct and others8 Abstract 论文指出恶意微调防御在持续训练下均会衰减,表明基于有限攻击者假设的防御无法为开源模型提供持久保护。
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