When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models

📰 ArXiv cs.AI

Learn how over-alignment in vision-language models can lead to hallucinations and how geometric debiasing can help mitigate this issue, crucial for high-stakes applications like medical imaging and autonomous systems

advanced Published 12 May 2026

Action Steps

Investigate the root causes of hallucinations in vision-language models using mechanistic analysis
Identify geometric over-alignment as a key factor contributing to these failure modes
Apply geometric debiasing techniques to mitigate over-alignment and reduce hallucinations
Evaluate the effectiveness of debiasing methods in improving model performance and robustness
Implement decoder-based VLMs with debiasing mechanisms to improve their reliability in high-stakes applications

Who Needs to Know This

Researchers and engineers working on vision-language models, particularly those in the fields of medical imaging and autonomous systems, can benefit from understanding the causes of hallucinations in these models and how to address them

Key Insight

💡 Geometric over-alignment in vision-language models can lead to hallucinations, and debiasing techniques can help mitigate this issue

Key Takeaways

Full Article

Title: When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models

Abstract:
arXiv:2605.08245v1 Announce Type: 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, dec

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