Coupled or Independent? Fault Coupling in Agentic Medical Vision Systems
Read the original on arXiv Computer Vision →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.
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