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NeurIPS 2026U Georgianational university of singaore,New York UniveristyU SydneyNew Jersey Institute of TechnoloAdversarial robustness

MedVIGIL: Evaluating Trustworthy Medical VLMs Under Broken Visual Evidence

MedVIGIL evaluates medical vision-language models under broken visual evidence via clinician-supervised probes, revealing a 14.1-point gap between top models and radiologist reliability.

Hanqi Jiang, Junhao Chen, Yi Pan, Lifeng Chen and 9 more

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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17/20 AI panelreviewers recommend it

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AI panel: 17 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 4/5
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NeurIPS 2026New York UniveristyNew Jersey Institute of TechnoloVision-language models

NeuronEye: Query-Guided Visual Concept Activation for Vision-Language Reasoning

NeuronEye improves vision-language reasoning by selectively activating query-relevant sparse visual concept clusters and suppressing dominant cues during inference in frozen VLMs. It raises CV-Bench accuracy by +3.1 and BLINK Multi-view by +8.3 on Qwen2.5-VL-7B without retraining.

Ruiyu Yan, Bowen Chen, Shaowen Wan, Lin Zhao

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 1/5