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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 MedDistract 基准，测量偶发信息对临床文书与推理的污染
Published Oct 6
Evaluated models GPT-5.4 , GPT-5.6 Sol , Claude Opus 5 and others9 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 构建 Mandela-Bench，评测多模态模型是否凭记忆接受篡改图像
Published Sep 27
Evaluated models Gemini-3.8-Flash , GPT-5.6 , GLM-5.3-Flash and others15 Abstract 论文构建 Mandela-Bench 发现模型对经典图像存在记忆重构偏见，过度依赖先验导致无法有效利用知识核验事实篡改。该研究系统评估了多模态大语言模型在面对无痕事实性编辑时，能否利用自身内在世界知识核验被识别出的经典图像。
提出 KoNA 基准，评测视觉语言模型的选择性不遵从
Published Sep 4
Evaluated models InternVL3-2B-Instruct , InternVL3-78B-Instruct , Qwen2.5-VL-3B-Instruct and others8 Abstract KoNA 用五类成对查询评选择性不遵从。KoNA 用来评测视觉语言模型能否对混合查询做选择性不遵从：只处理不该遵从的成分，同时回答其余成分。
比较多模态大模型上三类免训练不确定性估计
Published Sep 1
Evaluated models InternVL2-2B , InternVL2-8B , InternVL3.5-2B and others8 Abstract 回答长度决定免训练 UE 族。这是一项对 MLLM 免训练不确定性估计的分组比较：幻觉检测被作者视为弃答、转交或路由的一种用途，而不是唯一目标。
在 Gut-VLM 上评测内镜 VLM 的幻觉检测方法
Published Jun 23
Evaluated models MedGemma-4B , MedGemma-27B , LLaVA-Med-7B and others5 Abstract 九种检测方法在 Gut-VLM 五模型上的比较中，ReXTrust 的 AUC 均为最高。这是一个胃肠道内镜 VQA 幻觉检测的比较基准，不是新检测器结构的提出。
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。
提出基准 CHART HAL，细粒度评测图表理解中的幻觉
Published Sep 22, 2025
Evaluated models Gemini-2.5-Pro , GPT-5 , GPT-5-mini and others15 You have reached the end