CIMR: Contextualized Iterative Multimodal Reasoning for Robust Instruction Following in LVLMs

• URL: http://arxiv.org/abs/2507.22074v1
• Date: Tue, 22 Jul 2025 18:39:18 GMT
• Title: CIMR: Contextualized Iterative Multimodal Reasoning for Robust Instruction Following in LVLMs
• Authors: Yangshu Yuan, Heng Chen, Xinyi Jiang, Christian Ng, Kexin Qiu,
• Abstract summary: CIMR is a novel framework that introduces a context-aware iterative reasoning and self-correction module.<n> CIMR achieves 91.5% accuracy, outperforming state-of-the-art models such as GPT-4V, LLaVA-1.5, MiniGPT-4, and InstructBLIP.
• Score: 2.238122883754112
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) has enhanced our ability to process and generate human language and visual information. However, these models often struggle with complex, multi-step multi-modal instructions that require logical reasoning, dynamic feedback integration, and iterative self-correction. To address this, we propose CIMR: Contextualized Iterative Multimodal Reasoning, a novel framework that introduces a context-aware iterative reasoning and self-correction module. CIMR operates in two stages: initial reasoning and response generation, followed by iterative refinement using parsed multi-modal feedback. A dynamic fusion module deeply integrates textual, visual, and contextual features at each step. We fine-tune LLaVA-1.5-7B on the Visual Instruction Tuning (VIT) dataset and evaluate CIMR on the newly introduced Multi-modal Action Planning (MAP) dataset. CIMR achieves 91.5% accuracy, outperforming state-of-the-art models such as GPT-4V (89.2%), LLaVA-1.5 (78.5%), MiniGPT-4 (75.3%), and InstructBLIP (72.8%), demonstrating the efficacy of its iterative reasoning and self-correction capabilities in complex tasks.

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