# Coupled or Independent? Fault Coupling in Agentic Medical Vision Systems

**Authors:** Nishi Kanta Paul, Md Shihabul Islam Shovo, Md Mostafijur Rahman, Jamil Fayyad  
**Lab:** American University of Sharjah · **Published:** 29 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.40913) · [PDF](https://arxiv.org/pdf/2610.40913) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.40913.html)

## Key numbers

- **55%**: …preserves the payload at a readable scale and produces 55% joint wrong agreement across all cases
- **71%**: …a readable scale and produces 55% joint wrong agreement across all cases (71% of faulty cases)
- **35%**: full-image retrieval produces 35%

## Abstract

Vision-language agents equipped with tools expose intermediate outputs for inspection and comparison with the model's reasoning, which may offer safety benefits over single-shot models. This paper tests that potential benefit when the sensor reading itself is untrusted, so that the tool and reasoning channels share a common visual fault. We consider a dermatology agent based on segmentation, contrastive retrieval, and an iterative vision-language reasoner and define a coupling taxonomy that distinguishes single-channel errors from joint errors that agree or disagree. On PAD-UFES-20, we show that typographic injection produces detectable directional retrieval capture, whereas physical corruption does not. Crop-first retrieval preserves the payload at a readable scale and produces 55% joint wrong agreement across all cases (71% of faulty cases); full-image retrieval produces 35% (43%). Under a binary decision, if both channels are wrong, their predictions must agree, so hard-label channel comparison cannot detect joint failures, limiting disagreement recall to 0.29 (crop-first) versus 0.57 (full-image). The direction of the order effect is reproduced on the independent HIBA test set.

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Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.