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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.
提出人格层级模型并设计 PPR 抑制奖励作弊的跨上下文泛化
Published Oct 7
Evaluated models Qwen3-4B , Llama-3.1-8B-Instruct Abstract 提出人格层级模型揭示微调跨上下文泛化规律，报告角色距离正相关性，通过 PPR 抑制 RL 作弊。
提出 HNC，在单个解附近遍历保函数权重并比较内部表示
Published Sep 30
Evaluated models 64-unit tanh RNN , ViT-S/16 , RNN policy (Plume Tracking) and others5 Abstract HNC 在保持函数的局部权重区域里找到表示、动力学或行为不同的解。Hessian Null Space Continuation（HNC） 用局部曲率，在单个已训练网络周围遍历近似保持输入输出的连通低损失区域，并可用可微目标做引导。作者要回答的是：这个区域里是否真有不同的内部机制，而不只是同一表示的小改动。做法是交替执行零空间内的平坦步与函数匹配损失上的梯度恢复。
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