ARTFEED — Contemporary Art Intelligence

Vision-Language Models Face Trustworthiness Crisis

ai-technology · 2026-04-24

A new paper on arXiv challenges the reliability of Vision-Language Models (VLMs), arguing that current models suffer from functional blindness by relying on language priors rather than grounded visual understanding. The authors propose a Modality Translation Protocol to quantify this issue.

Key facts

  • Paper title: The Expense of Seeing: Attaining Trustworthy Multimodal Reasoning Within the Monolithic Paradigm
  • Published on arXiv with ID 2604.20665
  • Critiques the Vision Encoder-Projector-LLM paradigm
  • Claims VLMs exhibit functional blindness
  • Proposes Modality Translation Protocol as a solution
  • Argues current evaluation methods conflate dataset biases with architectural incapacity
  • Takes an information-theoretic approach
  • Focuses on trustworthiness of multimodal reasoning

Entities

Institutions

  • arXiv

Sources