Is retrieval quality the main bottleneck?
Yes, for complex visual data like charts and tables, retrieval quality is the bigger limitation. A large benchmark on chart-based documents (Chart-MRAG Bench, with 4,738 question-answer pairs across 8 domains) found that current unified multimodal embedding retrieval methods struggle significantly in chart scenarios [2]. This means the system often fails to find the right chart or data in the first place, making any subsequent reasoning useless.
The same study showed that even when retrieval is perfect (ground-truth context provided), state-of-the-art multimodal large language models only achieve 71.15% Correctness and 80.74% Coverage scores [2]. While these numbers indicate room for reasoning improvement, the gap from perfect retrieval to perfect reasoning is smaller than the gap from typical retrieval to perfect retrieval. In other words, fixing retrieval yields bigger gains than improving reasoning alone.
How much does better retrieval improve the final answer?
Improving retrieval directly and substantially improves answer quality. A multimodal document processing system (SmartNotes) used a hybrid OCR approach and cross-encoder reranking, which improved retrieval precision by 17% [1]. This retrieval gain translated into a 48% improvement in answer accuracy compared to using a standalone large language model without retrieval [1]. The 48% jump is nearly three times the retrieval gain, showing that better retrieval has a multiplier effect on reasoning.
Another system for materials science literature achieved 92% accuracy in information retrieval, which enabled a retrieval-augmented chatbot to deliver quick, accurate responses (under 3 seconds for most queries) [3]. This system also showed a 35% improvement in similarity recognition over manual procedures and a 40% increase in knowledge synthesis efficiency [3]. These results reinforce that when retrieval works well, the overall system performance follows.
Are there cases where reasoning is the bigger problem?
Yes, reasoning can be a significant limitation, especially when the retrieved information is complex or multimodal. The chart benchmark revealed that even with perfect retrieval, top models still scored only 71% correctness, and they showed a consistent text-over-visual modality bias—meaning they relied too much on text and ignored visual information in charts [2]. This is a reasoning failure, not a retrieval failure.
However, the same study noted that retrieval methods themselves are poorly suited for chart data, which is information-dense and requires precise alignment between visual elements and text [2]. So the two problems compound: poor retrieval makes reasoning harder, and even good retrieval exposes reasoning weaknesses. A survey of multimodal RAG systems confirms that cross-modal alignment and reasoning remain open challenges, but notes that retrieval quality is the foundational step—if retrieval fails, reasoning has nothing to work with [5].
Can smarter retrieval fix the problem?
Yes, dynamic and instruction-aware retrieval methods can significantly improve retrieval quality, which in turn helps reasoning. A framework called InstructSee uses large language models to dynamically generate and refine query representations based on user instructions and feedback [4]. This approach outperformed fixed-query baselines on standard multimodal retrieval benchmarks, showing improved cross-modal alignment and adaptability [4].
This suggests that the retrieval bottleneck is not insurmountable—smarter retrieval that adapts to user intent can close the gap. The SmartNotes system also used a sophisticated vector search architecture with reranking, which directly improved retrieval precision and downstream answer accuracy [1]. Together, these studies indicate that investing in retrieval quality—through better embeddings, reranking, or dynamic query generation—pays off more than trying to improve reasoning alone.
About These Sources
This answer is built on 5 peer-reviewed studies — published from 2025 to 2026, 5 from 2024 or later, 1 in Q1–Q2 journals — selected as the most relevant from 5 studies that passed quality screening, drawn from 52 papers retrieved from a database of over 500 million.
Sources used in this answer
A Multimodal Retrieval-Augmented Generation System for Intelligent Document Processing and Study Assistance
A multimodal document processing system (SmartNotes) improved retrieval precision by 17% with cross-encoder reranking, which led to a 48% improvement in answer accuracy over standalone LLMs, in a user study with 15 participants.
Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents
A benchmark on chart-based documents (4,738 QA pairs, 8 domains) found that unified multimodal embedding retrieval methods struggle with charts, and even with perfect retrieval, top MLLMs achieve only 71.15% correctness and show a text-over-visual modality bias.
Automated Multimodal Knowledge Extraction for Economic Insights in Materials
An automated system for materials science literature achieved 92% retrieval accuracy, 35% improvement in similarity recognition over manual methods, and 40% faster knowledge synthesis, enabling a RAG chatbot with sub-3-second response times.
InstructSee: Instruction-Aware and Feedback-Driven Multimodal Retrieval with Dynamic Query Generation
A cross-modal retrieval framework (InstructSee) using LLM-based dynamic query generation and user feedback significantly improved retrieval accuracy and cross-modal alignment compared to fixed-query baselines on standard benchmarks.
Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation
A comprehensive survey of multimodal RAG systems identifies cross-modal alignment and reasoning as key challenges, but emphasizes that retrieval quality is foundational—if retrieval fails, reasoning has no useful input.