When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models
arXiv:2605.08245v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) increasingly power high-stakes applications, from medical imaging to autonomous systems, yet they routinely hallucinate, confidently describing content not present in the input. We investigate the root causes of these failure modes with a mechanistic analysis focusing on the decoder-based VLMs. We trace these failure modes to a geometric over-alignment: to bridge the modality gap required by attention mechanisms, decoder-based VLMs over-align visual embeddings with the text manifold, injecting a statistical
The proliferation of Vision-Language Models (VLMs) in high-stakes applications is exposing their inherent failure modes, making the investigation into these issues critical for model reliability and safety.
Understanding and mitigating VLM hallucinations is crucial for the safe and effective deployment of AI in critical sectors like autonomous systems and medical imaging, directly impacting trust and adoption.
The focus on geometric debiasing suggests a shift in VLM development towards addressing the fundamental causes of hallucination, moving beyond superficial fixes to improve model integrity.
- · AI safety researchers
- · Developers of robust VLM architectures
- · Industries reliant on high-accuracy VLMs (e.g., medical, autonomous vehicles)
- · VLM developers focusing solely on performance metrics
- · Applications with unmitigated VLM hallucination risks
Increased emphasis and funding for research into VLM reliability and hallucination mitigation.
Development of new VLM architectures or training methodologies specifically designed for geometric debiasing and reduced over-alignment.
Enhanced public and regulatory confidence in AI systems due to improved reliability of leading-edge VLMs, accelerating their integration into sensitive applications.
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Read at arXiv cs.AI