AGENTIC-SYSTEMSMarch 20, 20261 min read9 views

Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding

New research reveals a 'cognitive mismatch' in multimodal AI models, where they excel at complex reasoning but fail at basic symbol recognition, suggesting a reliance on linguistic probability over true visual perception.

Computer Science > Artificial Intelligence

Title:Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding

View PDF HTML (experimental)Abstract:While Multimodal Large Language Models (MLLMs) have achieved remarkable success in interpreting natural scenes, their ability to process discrete symbols -- the fundamental building blocks of human cognition -- remains a critical open question. Unlike continuous visual data, symbols such as mathematical formulas, chemical structures, and linguistic characters require precise, deeper interpretation. This paper introduces a comprehensive benchmark to evaluate how top-tier MLLMs navigate these "discrete semantic spaces" across five domains: language, culture, mathematics, physics, and chemistry. Our investigation uncovers a counterintuitive phenomenon: models often fail at basic symbol recognition yet succeed in complex reasoning tasks, suggesting they rely on linguistic probability rather than true visual perception. By exposing this "cognitive mismatch", we highlight a significant gap in current AI capabilities: the struggle to truly perceive and understand the symbolic languages that underpin scientific discovery and abstract thought. This work offers a roadmap for developing more rigorous, human-aligned intelligent systems.

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Source: arXiv cs.AI Recent

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