Translation in progress Some entries still include text in the original language.
Oct 13 · Tuesday
Another 227 papers do not have category tags yet and are not included in filtered results.
“
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 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 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。
You have reached the end