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

2026-01-06

arXiv.org

AI Summary

This work addresses the challenge of large language models (LLMs) struggling to interpret cartoon visual question answering (VQA) due to exaggerated visual styles and complex narrative contexts. To this end, we propose the first multi-agent LLM framework specifically designed for cartoon VQA, comprising three collaborative agents: a visual agent for feature extraction, a language agent for semantic understanding, and a critic agent for reasoning correction. This architecture enables structured cross-modal reasoning by explicitly delineating each agent’s role in the multimodal inference process, thereby advancing the understanding of how LLMs reason over non-photorealistic imagery. Experiments on the Pororo and Simpsons datasets demonstrate that our framework significantly improves answer accuracy, and ablation studies confirm the effectiveness of each component.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Computer Vision: Large Vision ModelsNatural Language Processing: (Large) Language Models

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved informationEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Cartoon VQA
• Visual Abstraction
• Narrative Context
• Large Language Models
• Multimodal Reasoning

Innovation

• multi-agent LLM
• cartoon VQA
• structured reasoning
• visual abstraction
• narrative context