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Updated: May 1, 2026

Humor or Rationality? The Neural Mechanisms of How Agent Type and Language Style Influence Satisfaction with Ride-Hailing Service Failure Recovery
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When 'YES' Meets 'but': Can AI Comprehend Contradictory Humor in Comics?

Tuo Liang, Zhe Hu, Jing Li

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    Summary
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    Large vision language models (VLMs) struggle with humor, especially contradictory comics. New benchmark YESBUT reveals significant performance gaps compared to humans, highlighting needs for better reasoning and cultural understanding in AI.

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    Area of Science:

    • Artificial Intelligence
    • Cognitive Science
    • Computational Linguistics

    Background:

    • Large vision language models (VLMs) face challenges in understanding complex humor requiring comparative reasoning.
    • This limitation impacts AI's capacity for human-like reasoning and cultural expression.

    Purpose of the Study:

    • To analyze humor in comics that use contradictory juxtapositions.
    • To introduce and utilize the YESBUT benchmark for evaluating VLMs' narrative and comparative reasoning abilities.
    • To identify specific weaknesses in current VLM performance.

    Main Methods:

    • Development of the YESBUT benchmark (1,262 comic images) with diverse contexts and annotations.
    • Systematic evaluation of various VLMs on four tasks, focusing on comparative reasoning.
    • Investigation of text-based training and social knowledge augmentation strategies.

    Main Results:

    • Advanced VLMs significantly underperform compared to human capabilities in understanding comic humor.
    • Common failure points include visual perception, key element identification, comparative analysis, and hallucinations.
    • Current models lack deep narrative understanding, especially with contradictory elements.

    Conclusions:

    • VLMs exhibit critical weaknesses in understanding cultural and creative expressions, particularly humor based on contradiction.
    • The YESBUT benchmark provides a pathway for assessing and improving VLM narrative reasoning.
    • Future research should focus on developing context-aware models with enhanced comparative reasoning skills.