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Oct 12 · Monday
All papers Found 10 papers Another 233 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 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 提出训练无关的 SECRET，对问题表征做源条件转向以缓解音视频模型幻觉
Published Sep 29
Evaluated models VideoLLaMA2-AV-7B , Qwen2.5-Omni-7B , Qwen3-Omni-30B-A3B Abstract SECRET 在问题状态上做源条件转向，三个 AVLLM 上减轻了跨模态接地幻觉。SECRET 是一种训练无关的问题状态转向方法，用来减轻 AVLLM 的 source-confused grounding hallucination。
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。
用门控 IoU 与 RadGraph 的 GRPO 降低胸片报告幻觉
Published Sep 26
Evaluated models Qwen3-VL-8B-GRPO Propose the KoNA benchmark to evaluate selective non-compliance in vision-language models
Published Sep 4
Evaluated models InternVL3-2B-Instruct , InternVL3-78B-Instruct , Qwen2.5-VL-3B-Instruct and others8 Abstract KoNA evaluates selective noncompliance with five types of paired queries.KoNA is used to evaluate whether vision-language models can selectively refuse mixed queries: only handle the components that should not be followed, while answering the remaining components.
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。
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 SPAR 免训练注意力干预，削弱多模态模型的结构偏置
Published Jul 27
Evaluated models LLaVA-1.5-7B , LLaVA-NeXT Abstract SPAR 以结构偏置净化缓解视觉幻觉，结果需区分误导后收益与原始任务例外。SPAR 是一种无需训练、直接修改多模态内部注意力的幻觉防御。 作者关注视觉信息被查询不变的注意力偏置稀释，以及误导文本进一步推动模型依赖语言先验的问题。
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