The paper investigates how memory systems can answer a current query correctly yet fail to retain distinctions needed for later updates. Using a paired‑history audit, the authors evaluate 24 history pairs across six synthetic mechanisms and two model backends, achieving perfect reveal accuracy on DeepSeek and high accuracy on GLM. Record‑level audits reveal specific failures in structured reveal memories and frontier late‑reference adequacy, and the authors test a label‑equivariant repair that only partially restores correctness.
By Guangzhe Zhang
arXiv:2603. 28387v2 Announce Type: replace Abstract: Trustworthy clinical AI requires that performance gains reflect genuine evidence integration rather than surface-level artifacts.
By Doan Nam Long Vu, Simone Balloccu
arXiv:2606. 15910v2 Announce Type: replace Abstract: A vision-language model can answer a question about a chest radiograph or a pathology slide fluently and confidently while barely using the image, relying instead on language priors.
By Reza Khanmohammadi, Kundan Thind, Mohammad M. Ghassemi
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
arXiv:2607. 16303v1 Announce Type: cross Abstract: Medical Vision-Language Models (Med-VLMs) require reliable reasoning from fine-grained visual evidence, yet existing models can produce plausible clinical answers by relying on language priors or medical templates rather than truly attending to diagnosis-critical regions.
By Yunhang Qian, Jiaquan Yu, Jiawei Liu, Meng Wang, Hongwei Bran Li, Xiaobin Hu
arXiv:2604.02543v2 Announce Type: replace
Abstract: As vision-language models (VLMs) are increasingly deployed in clinical decision support, more than accuracy is required: knowing when to trust thei...
By Ji Young Byun, Young-Jin Park, Jean-Philippe Corbeil, Asma Ben Abacha
arXiv:2604. 08941v2 Announce Type: replace Abstract: Medical Vision-Language Models (VLMs) answering binary presence questions on chest radiographs can fail in two linked ways: they are confidently wrong, and they change answers when a clinically equivalent question is rephrased.
By Binesh Sadanandan, Vahid Behzadan
arXiv:2609.21755v1 Announce Type: new
Abstract: Emergency department (ED) decision-making relies on heterogeneous clinical information, including patient history, vital signs, laboratory results, and...
By Jinning Liang, Mingcheng Zhu, Tingting Zhu
Vision–language models can correctly diagnose medical questions using only the clinical vignette, without the diagnostic image. In experiments on 60 Italian State Examination items, models defended diagnoses derived from a blank placeholder as firmly as those derived from real images, showing no increased fragility. While different adversarial pressures produced distinct dynamics—one leading to a single decisive flip and the other to continual changes—the instability under contentless doubt reflected item ambiguity rather than lack of evidence.
By Federico Felizzi, Francesco Andrea Causio, Bianca Destro Castaniti, Olivia Riccomi, Michele Ferramola, Vittorio De Vita, Alessandro Tosi, Antonio Cristiano, Alessia Longo, Lorenzo De Mori, Chiara Battipaglia, Melissa Sawaya, Luigi De Angelis, Marcello Di Pumpo, Alessandra Piscitelli, Pietro Eric Risuleo, Giulia Vojvodic, Mariapia Vassalli, Nicolo Scarsi, Manuel Del Medico
The paper introduces HAD‑VLM, a hybrid autoregressive‑diffusion vision‑language model that jointly generates text prompts and predicts continuous ultrasound measurements. By tuning the vision encoder with visual prompts and employing a diffusion process to output caliper coordinates and lengths, the model achieves lower mean absolute errors on four echocardiographic tasks compared to autoregressive and fine‑tuned baselines. It also maintains medical question‑answering performance and improves vertebral measurement accuracy on X‑ray data.
By Dianye Huang, Jiamei Guo, Yue Zhou, Mingde Li, Yi Xiong, Wei Wang, Nassir Navab, Zhongliang Jiang
arXiv:2608. 05670v1 Announce Type: new Abstract: A model's agreement across perturbed inputs is used both as a label-free reliability signal and as a self-training target, on the premise that agreement tracks correctness.
By Rasul Khanbayov, Hasan Kurban
arXiv:2607. 01973v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly applied in medical tasks such as pathology description, report generation, and visual question answering.
By Sofiane Ouaari, Kevin Vorwalder, Nico Pfeifer