{"data":{"id":15,"backendId":"e000ab88-a7c6-4717-ae2a-d9aec3e6e62c","title":"Beyond Accuracy: Evaluating Visual Grounding In Multimodal Medical Reasoning","summary":"arXiv:2603.03437v1 Announce Type: new Abstract: Recent work shows that text-only reinforcement learning with verifiable rewards (RLVR) can match or outperform image-text RLVR on multimodal medical VQA benchmarks, suggesting current evaluation protocols may fail to measure causal visual dependence. We introduce a counterfactual evaluation framework using real, blank, and shuffled images across four medical VQA benchmarks: PathVQA, PMC-VQA, SLAKE, and VQA-RAD. Beyond accuracy, we measure Visual Re","analysis":"This paper provides critical, actionable metrics for a major flaw in multimodal AI: models achieving high accuracy through text-only shortcuts.","category":"technology","strategicTrack":"ai_agents","capitalRelevance":{"social":2,"cultural":3,"economic":5,"symbolic":4,"technological":10,"informational":9,"temporal":7,"psychological":2,"physical":2},"tags":["Medical AI","Multimodal Learning","RLVR","Visual Grounding","Model Evaluation"],"qualityScore":10,"valueScore":9,"interestScore":8,"potentialScore":9,"uniquenessScore":9,"sourceCount":1,"confidence":5,"detectedAt":"2026-03-05T18:09:05.125Z","createdAt":"2026-03-05 18:10:47"}}