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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.
构建多语言音视频 DeepFake 基准 BabelFake 并评测检测器
Published Oct 5
Evaluated models SpeechForensics , AVH-Align , (B)RAVEn Abstract BabelFake 为五语同意采集基准。BabelFake 是经同意与 IRB 批准采集的多语音视频 DeepFake 检测基准，用来把视觉操纵和语音合成拆开比较。
提出 ControlPed 与 HazardPed，生成可控行人危险场景并评测端到端驾驶
Published Oct 5
Evaluated models DiffusionDrive , SSR , VAD and others7 分析文本水印在 RAG 中诱发事实幻觉的机制并提出干预
Published Oct 4
Evaluated models LLaMA 3.1 8B Instruct , Mistral-7B-Instruct-v0.3 , Qwen2.5-14B-Instruct Abstract 论文揭示了生产环境文本水印导致模型事实退化的失效模式，分析了解码机制并提出针对性干预。论文研究生产级文本水印对大语言模型事实准确性的影响，分析了水印诱发幻觉的失效模式并评估了针对性缓解方案。
提出 OmniConfess，冻结候选并按通道重打分以缓解全模态幻觉
Published Oct 2
Evaluated models Qwen2.5-Omni-7B , Qwen3-Omni-30B-A3B , Nemotron-3-Nano-Omni-30B 提出 KPI，用入度定位跨层关键路径并做 SAE 转向以解决知识冲突
Published Sep 8
Evaluated models Llama-3-8B , Llama-3.1-8B , Gemma-2-9B Abstract KPI 以入度关键路径替代大批 SAE 特征转向，在冲突 RAG 上提高忠实并减轻副作用。KPI 针对知识冲突 RAG 中的 SAE 转向：相关性批量改特征会带入冗余，少量因果上关键的特征即可完成转向。
用 SAE 追踪稠密检索查询扩展的潜变量并用激活转向验证
Published Sep 7
Evaluated models Qwen3-Embedding-0.6B（Qwen-Embed） , SimLM Abstract SAE 追踪稠密检索 QE 的潜变量，转向在四基准上比文本扩展更稳。作者用稀疏自编码器分析稠密检索里的查询扩展：现成检索器不微调时，拼接扩展文本往往增益有限甚至变差，而最终指标和嵌入整体偏移不足以说明内部发生了什么。
提出 Artifact-Bench，评测 MLLM 检测并诊断 AI 生成视频伪影
Published May 19
Evaluated models Gemini 3.1 Pro , Gemini 3 Flash , Qwen3-VL 8B-Instruct and others19 Abstract Artifact-Bench 以三项任务评 MLLM 的 AIGC 伪影感知。Artifact-Bench 用来看 MLLM 能否发现 AI 生成视频中的伪影，并说明视频为何不真实。
提出 DIQ-H 基准，评测 VLM 在连续退化视觉流中的幻觉残留与恢复
Published Dec 4, 2025
Evaluated models LLaVA-7B-strict , InternVL2-8B-strict , Phi-3-Vision-strict and others17 Abstract DIQ-H 用连续退化视频测幻觉延续与恢复，VIR 用不确定性过滤生成伪标注。DIQ-H 评测 VLM 在连续视觉退化中的幻觉、恢复和跨时间一致性。VIR 用轻量模型的不确定性过滤生成伪真值。
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