标题： 理解大型语言模型在卡通视觉问答中的多智能体推理 显示英文标题

标题： Understanding Multi-Agent Reasoning with Large Language Models for Cartoon VQA

摘要： 视觉问答（VQA）用于风格化的卡通图像面临挑战，例如解释夸张的视觉抽象和叙事驱动的上下文，这些在标准大型语言模型（LLMs）上无法得到充分解决，这些模型是在自然图像上训练的。 为研究这一问题，引入了一个多智能体LLM框架，专门设计用于卡通图像中的VQA任务。 所提出的架构包括三个专业智能体：视觉智能体、语言智能体和批评智能体，它们协同工作，通过整合视觉线索和叙事上下文来支持结构化推理。 该框架在两个基于卡通的VQA数据集：Pororo和Simpsons上进行了系统评估。 实验结果提供了对每个智能体如何贡献最终预测的详细分析，加深了对基于LLM的多智能体行为在卡通VQA和多模态推理中的理解。 显示英文摘要

摘要： Visual Question Answering (VQA) for stylised cartoon imagery presents challenges, such as interpreting exaggerated visual abstraction and narrative-driven context, which are not adequately addressed by standard large language models (LLMs) trained on natural images. To investigate this issue, a multi-agent LLM framework is introduced, specifically designed for VQA tasks in cartoon imagery. The proposed architecture consists of three specialised agents: visual agent, language agent and critic agent, which work collaboratively to support structured reasoning by integrating visual cues and narrative context. The framework was systematically evaluated on two cartoon-based VQA datasets: Pororo and Simpsons. Experimental results provide a detailed analysis of how each agent contributes to the final prediction, offering a deeper understanding of LLM-based multi-agent behaviour in cartoon VQA and multimodal inference.