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CRAFT: Causal Responsibility and Failure Tracing in Medical Vision Language Models

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arXiv AI

3d ago

CRAFT: Causal Responsibility and Failure Tracing in Medical Vision Language Models

arXiv:2609.38810v1 Announce Type: cross Abstract: As vision language models are increasingly deployed in clinical diagnosis, understanding how they internally resolve competing visual and textual sig...

By Chunzheng Zhu, Jiaqi Zeng, Hongbo Zhao, Yihang Chen, Yijun Wang, Jianxin Lin

llms multimodal benchmarks safety

arXiv Machine Learning

Jul 7

Pathways of Visual Information Flow in Vision-Language Models

arXiv:2607. 03358v1 Announce Type: cross Abstract: We study how visual information is routed in vision-language models (VLMs).

By Israfel Salazar, Stella Frank, Dan Oneata, Desmond Elliott, Constanza Fierro

llms multimodal benchmarks

arXiv Machine Learning

Sep 1

Do VLMs Share Safety Neurons Across Modalities?

arXiv:2608.30750v1 Announce Type: new Abstract: Vision-language models (VLMs) can comply with harmful requests delivered through images, even when their LLM backbones would refuse the same content in...

By Jiaxuan Li, Jiahao Zhang, Duc Minh Vo, Huy H. Nguyen, Pride Kavumba, Koki Wataoka

llms multimodal benchmarks safety

arXiv AI

Jun 17

Visuals Lie, Consistency Speaks: Disentangling Spatial Attention from Reliability in Vision-Language Models

arXiv:2606. 17389v1 Announce Type: cross Abstract: Multimodal Foundation Models are increasingly used as reasoning agents, making reliability, knowing when a model may hallucinate, critical.

By Logan Mann, Yi Xia, Ajit Saravanan, Ishan Dave, Saadullah Ismail, Shikhar Shiromani, Emily Huang, Ruizhe Li, Kevin Zhu

llms agents multimodal

arXiv AI

Jun 29

Dismantling Pathological Shortcuts: A Causal Framework for Faithful LVLM Decoding

arXiv:2606. 27596v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) exhibit sophisticated reasoning but remain susceptible to object hallucination.

By Liu Yu, Can Chen, Ping Kuang, Zhikun Feng, Fan Zhou, Gillian Dobbie

llms multimodal benchmarks safety

arXiv AI

Jun 26

TAVR-VLM: Risk-Conditioned Causal Grounding for Hallucination-Resistant Report Generation

arXiv:2606. 26874v1 Announce Type: new Abstract: Transcatheter Aortic Valve Replacement (TAVR) planning requires meticulous multimodal reasoning.

By Zhixiang Lu, Xiwei Liu, Sifan Song, Changkai Ji, Anh Nguyen, Jionglong Su, Imran Razzak, Jinfeng Wang

llms multimodal benchmarks safety

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CRAFT: Causal Responsibility and Failure Tracing in Medical Vision Language Models

Pathways of Visual Information Flow in Vision-Language Models

Do VLMs Share Safety Neurons Across Modalities?

Visuals Lie, Consistency Speaks: Disentangling Spatial Attention from Reliability in Vision-Language Models

Dismantling Pathological Shortcuts: A Causal Framework for Faithful LVLM Decoding

TAVR-VLM: Risk-Conditioned Causal Grounding for Hallucination-Resistant Report Generation