The Expense of Seeing: Attaining Trustworthy Multimodal Reasoning Within the Monolithic Paradigm

Hugging Face Daily Papers Papers
vision-language-models multimodal-reasoning trustworthiness functional-blindness information-theory evaluation-metrics

Summary

This paper challenges the assumption that current Vision-Language Models faithfully synthesize multimodal data, proposing an information-theoretic Modality Translation Protocol with new metrics (Toll, Curse, Fallacy of Seeing) to evaluate trustworthiness over traditional multimodal gain.

The rapid proliferation of Vision-Language Models (VLMs) is often framed as enabling unified multimodal knowledge discovery but rests on an under-examined assumption: that current VLMs faithfully synthesise multimodal data. We argue they often do not, and this gap reflects a trustworthiness problem in 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 conflates dataset biases with architectural incapacity. We propose an information-theoretic departure: the Modality Translation Protocol, designed to quantify what we call 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 hypothesise a Divergence Law of Multimodal Scaling: as the underlying language engines scale to unprecedented reasoning capabilities, the penalty of the visual knowledge bottleneck may increase rather than diminish. We argue the community should move beyond "multimodal gain" as a primary evaluation target. By elevating the SSC from a passive diagnostic constraint to an active architectural blueprint, we provide a foundation for guiding the next generation of AI systems toward genuine multimodal reasoning.
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Source: https://huggingface.co/papers/2604.20665

Abstract

Vision-Language Models often fail to faithfully synthesize multimodal data due to reliance on language priors over visual representation, necessitating new evaluation frameworks that prioritize semantic sufficiency over traditional multimodal gain metrics.

The rapid proliferation ofVision-Language Models(VLMs) is often framed as enabling unified multimodal knowledge discovery but rests on an under-examined assumption: that current VLMs faithfully synthesise multimodal data. We argue they often do not, and this gap reflects a trustworthiness problem in the dominantVision Encoder-Projector-LLM paradigm. Rather than extracting grounded knowledge from visual inputs, state-of-the-art models frequently exhibitfunctional blindness, i.e., exploiting strong language priors to bypass severe visual representation bottlenecks. In this work, we challenge the conventional methodology ofmultimodal evaluation, which relies on data ablation or new dataset creation and therefore conflates dataset biases with architectural incapacity. We propose an information-theoretic departure: theModality Translation Protocol, designed to quantify what we call theExpense of Seeing. By translating semantic payloads rather than ablating them, we formulate three novel metrics -- theToll (ToS),Curse (CoS), andFallacy (FoS)of Seeing -- culminating in theSemantic Sufficiency Criterion (SSC). Furthermore, we hypothesise aDivergence Law of Multimodal Scaling: as the underlying language engines scale to unprecedented reasoning capabilities, the penalty of the visual knowledge bottleneck may increase rather than diminish. We argue the community should move beyond “multimodal gain” as a primary evaluation target. By elevating the SSC from a passive diagnostic constraint to an active architectural blueprint, we provide a foundation for guiding the next generation of AI systems toward genuine multimodal reasoning.

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