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Oct 12 · Monday
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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 ProbeGuard，按共识形成过程对医疗问答做选择性弃答
Published Oct 6
Evaluated models Qwen3-8B , Llama-3.1-8B-Instruct , HuatuoGPT-o1-8B Abstract ProbeGuard 用共识形成过程做可认证弃权，并在全票层分开答对与答错的共识。ProbeGuard 是给多轮共识医学问答用的认证弃权框架：基板照原样作答，弃权只看共识是怎么形成的。
提出 Sentinel-RL，将拓扑处置从 LLM 卸载到受约束图策略
Published Sep 4
Evaluated models HetGAT , PPO Abstract Sentinel-RL 用图编码器与 PPO 承担拓扑决策，LLM 负责叙述与门控，并在 LANL 上报告检测与时延。Sentinel-RL 是面向 SOC 的神经符号架构：拓扑决策离开 LLM，改由认证图上的 HetGAT 与 PPO 完成。
提出内生护栏 SingProbe，复用解码隐状态监测查询意图、回复安全与幻觉
Published Aug 31
Evaluated models Ling-3.0-tiny-singprobe , Ling-3.0-flash-singprobe Abstract SingProbe 用轻量内生探针做生成时三类监测，并给出流式基准与低开销服务接入。SingProbe 是面向生成时监测的开源内生护栏：复用基座解码隐状态，而不是另起模型反复编码文本。
提出 VisER，用内部视觉证据检测 LVLM 物体幻觉
Published Aug 31
Evaluated models LLaVA-1.5-7B , LLaVA-NeXT-7B , InstructBLIP and others8 Abstract VisER 用证据门与图像–前缀比给物体提及打落地分。VisER 是面向 LVLM 开放式图像描述的免训练物体级幻觉检测分数，用来处理内部支持的来源混淆。
提出 DeMTS，把扩散语言模型去噪轨迹建成多元时间序列以检测幻觉
Published Jul 24
Evaluated models LLaDA-8B-Instruct , Dream-7B-Instruct You have reached the end