Efficient Agent: Optimizing Planning Capability for Multimodal Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2508.08816v1
• Date: Tue, 12 Aug 2025 10:17:12 GMT
• Title: Efficient Agent: Optimizing Planning Capability for Multimodal Retrieval Augmented Generation
• Authors: Yuechen Wang, Yuming Qiao, Dan Meng, Jun Yang, Haonan Lu, Zhenyu Yang, Xudong Zhang,
• Abstract summary: Multimodal Retrieval-Augmented Generation (mRAG) has emerged as a promising solution to address the temporal limitations of Multimodal Large Language Models (MLLMs) in real-world scenarios.<n>We propose E-Agent, an agent framework featuring two key innovations: a mRAG planner trained to dynamically orchestrate multimodal tools based on contextual reasoning and a task executor employing tool-aware execution sequencing.
• Score: 17.115587821286223
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multimodal Retrieval-Augmented Generation (mRAG) has emerged as a promising solution to address the temporal limitations of Multimodal Large Language Models (MLLMs) in real-world scenarios like news analysis and trending topics. However, existing approaches often suffer from rigid retrieval strategies and under-utilization of visual information. To bridge this gap, we propose E-Agent, an agent framework featuring two key innovations: a mRAG planner trained to dynamically orchestrate multimodal tools based on contextual reasoning, and a task executor employing tool-aware execution sequencing to implement optimized mRAG workflows. E-Agent adopts a one-time mRAG planning strategy that enables efficient information retrieval while minimizing redundant tool invocations. To rigorously assess the planning capabilities of mRAG systems, we introduce the Real-World mRAG Planning (RemPlan) benchmark. This novel benchmark contains both retrieval-dependent and retrieval-independent question types, systematically annotated with essential retrieval tools required for each instance. The benchmark's explicit mRAG planning annotations and diverse question design enhance its practical relevance by simulating real-world scenarios requiring dynamic mRAG decisions. Experiments across RemPlan and three established benchmarks demonstrate E-Agent's superiority: 13% accuracy gain over state-of-the-art mRAG methods while reducing redundant searches by 37%.

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