aiqbench analysis
This paper is squarely about AI: it proposes a metric and benchmark for explaining how Vision-Language Models reason across images and text, and it evaluates nine XAI methods. The key finding is that existing explainers for VLMs tend to highlight visual salience and miss genuine cross-modal interaction, which matters because unimodal evaluation metrics can mislead auditors. For practitioners and researchers, the useful takeaway is that VLM explanations need interaction-aware evaluation before they are trusted in high-stakes settings, though the results still rest on a limited set of architectures and datasets.
Measuring Cross-Modal Synergy: A Benchmark for VLM Explainability
arXiv:2605.22168v2 Announce Type: replace-cross 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. 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 ($\rho = 0.93$) while achieving up to $88\times$ computational speedup. Evaluating 9 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. Code: https://github.com/serval-uni-lu/xai-vlms-benchmark
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. 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 ($\rho = 0.93$) while achieving up to $88\times$ computational speedup. Evaluating 9 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. Code: this https URL
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2605.22168 [cs.AI]
(or arXiv:2605.22168v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.22168 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 275 words