Analyze-Prompt-Reason: A Collaborative Agent-Based Framework for Multi-Image Vision-Language Reasoning

• URL: http://arxiv.org/abs/2508.00356v1
• Date: Fri, 01 Aug 2025 06:39:15 GMT
• Title: Analyze-Prompt-Reason: A Collaborative Agent-Based Framework for Multi-Image Vision-Language Reasoning
• Authors: Angelos Vlachos, Giorgos Filandrianos, Maria Lymperaiou, Nikolaos Spanos, Ilias Mitsouras, Vasileios Karampinis, Athanasios Voulodimos,
• Abstract summary: We present a Collaborative Agent-Based Framework for Multi-Image Reasoning.<n>Our approach tackles the challenge of interleaved multimodal reasoning across diverse datasets and task formats.<n>We evaluate our method on 18 diverse datasets from the 2025 MIRAGE Challenge.
• Score: 3.588567067449924
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a Collaborative Agent-Based Framework for Multi-Image Reasoning. Our approach tackles the challenge of interleaved multimodal reasoning across diverse datasets and task formats by employing a dual-agent system: a language-based PromptEngineer, which generates context-aware, task-specific prompts, and a VisionReasoner, a large vision-language model (LVLM) responsible for final inference. The framework is fully automated, modular, and training-free, enabling generalization across classification, question answering, and free-form generation tasks involving one or multiple input images. We evaluate our method on 18 diverse datasets from the 2025 MIRAGE Challenge (Track A), covering a broad spectrum of visual reasoning tasks including document QA, visual comparison, dialogue-based understanding, and scene-level inference. Our results demonstrate that LVLMs can effectively reason over multiple images when guided by informative prompts. Notably, Claude 3.7 achieves near-ceiling performance on challenging tasks such as TQA (99.13% accuracy), DocVQA (96.87%), and MMCoQA (75.28 ROUGE-L). We also explore how design choices-such as model selection, shot count, and input length-influence the reasoning performance of different LVLMs.

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