The Expense of Seeing: Attaining Trustworthy Multimodal Reasoning Within the Monolithic Paradigm
Karan Goyal, Dikshant Kukreja
Why It Matters
What makes this one worth your time
Understanding the limitations of VLMs is crucial for developing more reliable AI systems that can effectively integrate and reason across multiple modalities.
This work challenges the reliability of Vision-Language Models and proposes a new framework for evaluating multimodal reasoning.
Summary
The paper critiques the current Vision-Language Models (VLMs) for their lack of trustworthiness in multimodal reasoning and proposes a new evaluation methodology called the Modality Translation Protocol, introducing metrics to assess visual knowledge synthesis.
Key contributions
- Introduction of the Modality Translation Protocol for multimodal evaluation.
- Development of three novel metrics: Toll (ToS), Curse (CoS), and Fallacy (FoS) of Seeing.
- Proposition of the Divergence Law of Multimodal Scaling to explain the increasing visual knowledge bottleneck.
Notable insights
- The concept of 'functional blindness' highlights a critical flaw in current VLMs that rely heavily on language priors, potentially undermining their multimodal capabilities.
- The introduction of the Semantic Sufficiency Criterion (SSC) as an active architectural blueprint could reshape how future models are designed and evaluated.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2604.20665v1 Announce Type: cross Abstract: The rapid proliferation of Vision-Language Models (VLMs) is widely celebrated as the dawn of unified multimodal knowledge discovery but its foundation operates on a dangerous, unquestioned axiom: that current VLMs faithfully synthesise multimodal data. We argue they do not. Instead, a profound crisis of trustworthiness underlies the dominant Vision Encoder-Projector-LLM paradigm. Rather than extracting grounded knowledge from visual inputs, state-of-the-art models frequently exhibit functional blindness, i.e., exploiting strong language priors to bypass severe visual representation bottlenecks. In this work, we challenge the conventional methodology of multimodal evaluation, which relies on data ablation or new dataset creation and therefore fatally conflates dataset biases with architectural incapacity. We propose a radical, information-theoretic departure: the Modality Translation Protocol, designed to quantifiably unmask the Expense of Seeing. By translating semantic payloads rather than ablating them, we formulate three novel metrics -- the Toll (ToS), Curse (CoS), and Fallacy (FoS) of Seeing -- culminating in the Semantic Sufficiency Criterion (SSC). Furthermore, we posit a provocative Divergence Law of Multimodal Scaling, hypothesising that as the underlying language engines scale to unprecedented reasoning capabilities, the mathematical penalty of the visual knowledge bottleneck paradoxically increases. We challenge the KDD community to abandon the illusory pursuit of "multimodal gain". By elevating the SSC from a passive diagnostic constraint to an active architectural blueprint, we provide the rigorous, trustworthy foundation required to force the next generation of AI systems to truly see the data, achieving true multimodal reasoning.