arXiv:2606.23763v1 Announce Type: cross Abstract: Recent work typically assesses vision--language consistency using attention distributions of answer-side tokens. However, we observe that highest attention regions are not always consistent with the intended semantic token. This probably stems from decoding drift, where language priors from previously generated answer tokens accumulate and mismatch with visual attention. Besides the priors from previous answer tokens, we find that structural tokens, e.g., modality boundary markers, may encompass the entire context and generate high attention to
This paper addresses a fundamental challenge (decoding drift) in Vision-Language Models (VLMs) at a time when these models are becoming increasingly central to AI research and applications.
Improving the consistency and reliability of VLM interpretations is crucial for their deployment in high-stakes applications and for advancing general AI capabilities.
The understanding and potential mitigation of 'decoding drift' in VLMs could lead to more robust, accurate, and trustworthy multimodal AI systems.
- · AI researchers focusing on multimodal models
- · Developers of VLM-powered applications
- · Companies investing in embodied AI and robotics
- · Computer vision and natural language processing fields
- · Developers relying solely on current attention mechanisms for VLM interpretation
- · Applications with high visual ambiguity reliance
VLMs become more accurate and less prone to semantic mismatches between vision and language.
This improved reliability accelerates the adoption of VLMs in critical domains like autonomous systems and medical imaging diagnostics.
Enhanced VLM capabilities contribute to breakthroughs in general artificial intelligence, enabling more sophisticated human-AI interaction and understanding.
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Read at arXiv cs.AI