🤖 AI Summary
Current vision-language models (VLMs) suffer from “functional blindness” in multimodal reasoning, overly relying on linguistic priors while neglecting visual inputs, thereby undermining their reliability. This work proposes an information-theoretic modality translation protocol that quantifies a model’s genuine use of visual information through semantic payload translation rather than data ablation. We introduce the novel concept of “visual toll” and define three new metrics—Toll, Curse, and Fallacy—alongside a Semantic Sufficiency Criterion (SSC). Our analysis reveals a divergence law in multimodal scaling, demonstrating that the visual bottleneck in mainstream VLMs intensifies as language model capacity grows. These findings provide both a theoretical foundation and a design blueprint for building trustworthy multimodal systems.
📝 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.