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Oct 12 · Monday

All papers

Found 11 papers
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Hallucination and fabrication6

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Output manipulation8

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Models and APIs10

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Prompt layer10Model layer8Application layer12Training and data layer3

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Guardrails and judges6Monitors5Reasoning chains6Vision11Audio6Video6Image generation3Multimodal21
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  3. Evaluation and benchmarksarXiv 2609.33481

    IDUnlearn-Bench：面向视觉语言模型的个体级遗忘基准

    提出 IDUnlearn-Bench，评测 VLM 个体级遗忘后的身份残留

    arXiv
    2609.33481
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Qwen3-VL-2B, Qwen3-VL-4B, Qwen3-VL-8B and others6
    AbstractIDUnlearn-Bench 用四族探针显示：压低直接属性访问后，身份仍常能被反向查出、绑定或重构。

    IDUnlearn-Bench 评测 VLM 是否忘掉一个可跨模态识别的人，而不只是答不出某条监督事实。

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  4. Evaluation and benchmarksarXiv 2609.32763

    Mandela-Bench: 36 multimodal models rely more on memory than on observing the image

    构建 Mandela-Bench，评测多模态模型是否凭记忆接受篡改图像

    arXiv
    2609.32763
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    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。

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  5. Evaluation and benchmarksarXiv 2609.04720

    Knowing What Not to Answer: Selective Non-Compliance in Vision-Language Models

    Propose the KoNA benchmark to evaluate selective non-compliance in vision-language models

    arXiv
    2609.04720
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    InternVL3-2B-Instruct, InternVL3-78B-Instruct, Qwen2.5-VL-3B-Instruct and others8
    AbstractKoNA 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.

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  6. Evaluation and benchmarksarXiv 2609.22206

    多模态大语言模型免训练不确定性估计的系统研究

    比较多模态大模型上三类免训练不确定性估计

    arXiv
    2609.22206
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    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。

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  7. Evaluation and benchmarksarXiv 2608.11816

    Study audits 9 vision-language models: national stance rewriting rate under Chinese prompts is about three times that under English

    审计视觉语言模型在敏感图像上从拒答转向国家立场重构的行为

    arXiv
    2608.11816
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Qwen2-VL-7B, Qwen2.5-VL-7B, Qwen3-VL-8B and others9
    Abstract论文系统审计了 VLM 多模态审查，发现审查正从显式拒答转向隐蔽重构，且中文提示与文本先验会放大重构倾向。

    论文系统研究了中外开源视觉语言模型在政治敏感图像上的审查行为，主要内容如下。

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  8. Evaluation and benchmarksarXiv 2606.24115

    Gut-VLM 基准：九种幻觉检测方法在胃肠道内镜 VLM 上的评测

    在 Gut-VLM 上评测内镜 VLM 的幻觉检测方法

    arXiv
    2606.24115
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    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。

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  9. Evaluation and benchmarksarXiv 2604.12447

    HazardArena：面向 VLA 模型的语义安全评测基准

    提出 HazardArena 基准，用孪生场景评测 VLA 的语义安全

    arXiv
    2604.12447
    Published
    Layer
    Model layer
    Setting
    Embodied systems and robotics
    Evaluated models
    OpenVLA-OFT, π0, NORA and others4
    Abstractsafe-only 微调提升良性执行，也提升匹配不安全孪生上的危险完成。

    HazardArena 评测 VLA 在运动要求不变时，能否因语义风险而停手。

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  10. Evaluation and benchmarksarXiv 2511.18921

    BackdoorVLM：面向视觉语言模型后门攻击与防御的基准

    提出 BackdoorVLM 基准，评测视觉语言模型微调中的后门攻击与防御

    arXiv
    2511.18921
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    LLaVA-1.5-7B, Qwen2.5-VL-7B
    AbstractBackdoorVLM 统一评测 VLM 后门与防御：含文本触发在低投毒率下更易激活，现有防御跨类型不稳定。

    BackdoorVLM 是作者提出的 VLM 后门攻击与防御评测基准，作者称它是首个此类综合基准。

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