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Beyond Accuracy: Evaluating Visual Grounding In Multimodal Medical Reasoning

technology ai_agents March 5, 2026 1 source · confidence 5/10

#Medical AI #Multimodal Learning #RLVR #Visual Grounding #Model Evaluation

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.

5D Score

Capital Relevance

technological

10/10

informational

9/10

temporal

7/10

economic

5/10

symbolic

4/10

cultural

3/10

social

2/10

psychological

2/10

physical

2/10

Agent API /api/v1/intel/15

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