The paper investigates how vision‑language agents that expose intermediate outputs can be affected when the sensor reading is unreliable, causing both the tool and reasoning channels to share a visual fault. Using a dermatology agent on the PAD‑UFES‑20 dataset, the authors introduce a fault‑coupling taxonomy distinguishing single‑channel errors from joint errors that agree or disagree, and show that typographic injection leads to detectable directional retrieval errors while physical corruption does not. They find that crop‑first retrieval yields a higher rate of joint wrong agreement (55%) compared to full‑image retrieval (35%), and that hard‑label channel comparison struggles to detect joint failures, limiting disagreement recall to 0.29 for crop‑first and 0.57 for full‑image retrieval, with similar effects observed on the HIBA test set.
arXiv:2607. 19793v1 Announce Type: new Abstract: Multimodal agentic search systems increasingly rely on external tools to answer knowledge-intensive visual questions.
As vision language models are increasingly deployed in clinical diagnosis, understanding how they internally resolve competing visual and textual signals becomes a safety imperative. Existing mechanis...
SalArt-VQA is a diagnostic benchmark that tests whether vision‑language models can understand salient artifacts in AI‑generated images. It includes 950 images and 3,681 multiple‑choice questions that assess artifact presence, semantic localization, spatial grounding, and evidence‑grounded defect identification. The benchmark reveals that high image‑level detection accuracy can mask failures in grounded understanding, showing a trade‑off between sensitivity and calibration.
arXiv:2610.27277v1 Announce Type: cross Abstract: Vision-language-action (VLA) models are sensitive to visual corruptions, but knowing that a corruption is typically harmful does not establish whethe...
Large vision-language models can recognize the objects and attributes in a crowded scene yet assign an attribute to the wrong same-class instance. Generic visual-question-answering accuracy marks the...
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...
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.
arXiv:2608. 10827v1 Announce Type: cross Abstract: Medical visual agents can use tools to inspect images and retrieve external knowledge, but indiscriminate tool use may introduce noisy or misleading evidence.
arXiv:2608. 16805v1 Announce Type: cross Abstract: Large vision-language models can recognize the objects and attributes in a crowded scene yet assign an attribute to the wrong same-class instance.
The paper introduces Mind2Web-Injection, a benchmark of 9,954 instruction‑screenshot pairs designed to evaluate whether vision‑language models (VLMs) use visual evidence when judging conflicts between on‑screen text and user instructions. Across six VLMs, models with similar overall precision can differ dramatically in Evidence‑Aligned Detection (EAD), revealing that verdict‑only metrics miss critical grounding failures. The authors also propose two training‑free interventions—ReadGate and CmdCompare—to improve evidence grounding and instruction‑command consistency, and argue for separate reporting of verdict correctness, evidence localization, and counterfactual responsiveness.
arXiv:2607. 16311v1 Announce Type: cross Abstract: Vision-language models (VLMs) often answer visual questions using learned language and category priors rather than grounding their predictions in the image itself.
arXiv:2610.32360v1 Announce Type: new Abstract: Vision-language models (VLMs) process images and text jointly, yet robustness evaluations often perturb these inputs separately or assess only predefin...