Fix Before Search: Benchmarking Agentic Query Visual Pre-processing in Multimodal Retrieval-augmented Generation

• URL: http://arxiv.org/abs/2602.13179v1
• Date: Fri, 13 Feb 2026 18:39:48 GMT
• Title: Fix Before Search: Benchmarking Agentic Query Visual Pre-processing in Multimodal Retrieval-augmented Generation
• Authors: Jiankun Zhang, Shenglai Zeng, Kai Guo, Xinnan Dai, Hui Liu, Jiliang Tang, Yi Chang,
• Abstract summary: V-QPP-Bench is the first benchmark dedicated to Visual Query Pre-processing.<n>Visual imperfections severely degrade both retrieval recall and end-to-end MRAG performance.<n>Off-the-shelf MLLMs struggle with tool selection and parameter prediction without specialized training.<n> supervised fine-tuning enables compact models to achieve comparable or superior performance to larger proprietary models.
• Score: 47.96044455071274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a key paradigm for grounding MLLMs with external knowledge. While query pre-processing (e.g., rewriting) is standard in text-based RAG, existing MRAG pipelines predominantly treat visual inputs as static and immutable, implicitly assuming they are noise-free. However, real-world visual queries are often ``imperfect'' -- suffering from geometric distortions, quality degradation, or semantic ambiguity -- leading to catastrophic retrieval failures. To address this gap, we propose V-QPP-Bench, the first comprehensive benchmark dedicated to Visual Query Pre-processing (V-QPP). We formulate V-QPP as an agentic decision-making task where MLLMs must autonomously diagnose imperfections and deploy perceptual tools to refine queries. Our extensive evaluation across 46,700 imperfect queries and diverse MRAG paradigms reveals three critical insights: (1) Vulnerability -- visual imperfections severely degrade both retrieval recall and end-to-end MRAG performance; (2) Restoration Potential \& Bottleneck -- while oracle preprocessing recovers near-perfect performance, off-the-shelf MLLMs struggle with tool selection and parameter prediction without specialized training; and (3) Training Enhancement -- supervised fine-tuning enables compact models to achieve comparable or superior performance to larger proprietary models, demonstrating the benchmark's value for developing robust MRAG systems The code is available at https://github.com/phycholosogy/VQQP_Bench

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