#diagnostic-framework2 articles

2 articles

AINeutralarXiv – CS AI · 5h ago

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RAG-X: Systematic Diagnosis of Retrieval-Augmented Generation for Medical Question Answering

Researchers propose RAG-X, a diagnostic framework for evaluating retrieval-augmented generation systems in medical AI applications. The study reveals an 'Accuracy Fallacy' showing a 14% gap between perceived system success and actual evidence-based grounding in medical question-answering systems.

AINeutralarXiv – CS AI · 5h ago

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When Visual Evidence is Ambiguous: Pareidolia as a Diagnostic Probe for Vision Models

Researchers developed a framework using face pareidolia (seeing faces in non-face objects) to test how different AI vision models handle ambiguous visual information. The study found that vision-language models like CLIP and LLaVA tend to over-interpret ambiguous patterns, while pure vision models remain more uncertain and detection models are more conservative.