Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability

• URL: http://arxiv.org/abs/2605.22168v1
• Date: Thu, 21 May 2026 08:39:46 GMT
• Title: Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability
• Abstract summary: Vision-Language Models (VLMs) map complex visual inputs to semantic spaces.<n>We introduce Synergistic Faithfulness ($mathcalF_syn$), a scalable metric rooted in the Shapley Interaction Index.
• Score: 16.886338830987462
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Vision-Language Models (VLMs) map complex visual inputs to semantic spaces, but interpreting the cross-modal reasoning of VLMs currently relies on post-hoc explainers evaluated via unimodal perturbation metrics. We expose a limitation in this paradigm: because multimodal datasets contain language priors and modality biases, VLMs frequently exhibit cross-modal redundancy, allowing them to answer visual queries using text alone. Consequently, unimodal metrics penalize faithful explainers, triggering an evaluation collapse where visual and textual rankings fundamentally contradict each other. %(Kendall's $τ= -0.06$). To resolve this, we introduce Synergistic Faithfulness ($\mathcal{F}_{syn}$), a scalable metric rooted in the Shapley Interaction Index that strictly isolates the joint Harsanyi dividend between modalities, serving as a highly accurate surrogate ($ρ= 0.92$) while achieving a $24\times$ computational speedup. Evaluating 8 distinct XAI methods across 3 VLM architectures and 3 benchmark datasets, reveals that explainers proposed for VLMs heavily over-index on visual salience and significantly underperform adapted attention-based methods in capturing true cross-modal synergy. By decoupling visual plausibility from cross-modal faithfulness, this work provides a rigorous evaluation framework required to safely audit VLM reasoning in high-stakes deployments.

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