arXiv:2605.22168v1 Announce Type: 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
The proliferation of Vision-Language Models (VLMs) and increasing demand for trustworthy AI necessitates robust explainability benchmarks, which this research aims to provide.
This research highlights a critical flaw in current VLM explainability metrics, suggesting that models may not be reasoning multi-modally but rather exploiting unimodal biases, which has significant implications for AI trustworthiness and deployment.
The proposed benchmark will force VLM developers to create more genuinely cross-modal reasoning architectures, rather than systems that merely leverage unimodal data redundancies.
- · AI ethicists
- · Developers of truly multimodal AI
- · Industries requiring high-trust AI
- · Developers relying on unimodal shortcuts
- · Users overestimating VLM capabilities
- · Current VLM explainability frameworks
Improved VLM explainability will lead to more reliable and deployable AI systems in sensitive applications.
The need for better multimodal reasoning may drive new architectural innovations in AI, moving beyond current transformer-based approaches.
Enhanced understanding of 'cross-modal synergy' could accelerate the development of more human-like general AI with deeper contextual understanding.
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Read at arXiv cs.LG