ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents (2502.18017v2)

Abstract: Understanding information from visually rich documents remains a significant challenge for traditional Retrieval-Augmented Generation (RAG) methods. Existing benchmarks predominantly focus on image-based question answering (QA), overlooking the fundamental challenges of efficient retrieval, comprehension, and reasoning within dense visual documents. To bridge this gap, we introduce ViDoSeek, a novel dataset designed to evaluate RAG performance on visually rich documents requiring complex reasoning. Based on it, we identify key limitations in current RAG approaches: (i) purely visual retrieval methods struggle to effectively integrate both textual and visual features, and (ii) previous approaches often allocate insufficient reasoning tokens, limiting their effectiveness. To address these challenges, we propose ViDoRAG, a novel multi-agent RAG framework tailored for complex reasoning across visual documents. ViDoRAG employs a Gaussian Mixture Model (GMM)-based hybrid strategy to effectively handle multi-modal retrieval. To further elicit the model's reasoning capabilities, we introduce an iterative agent workflow incorporating exploration, summarization, and reflection, providing a framework for investigating test-time scaling in RAG domains. Extensive experiments on ViDoSeek validate the effectiveness and generalization of our approach. Notably, ViDoRAG outperforms existing methods by over 10% on the competitive ViDoSeek benchmark. The code is available at https://github.com/Alibaba-NLP/ViDoRAG.

An Expert Analysis of ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents

The paper "ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents" addresses critical challenges in the integration of Retrieval-Augmented Generation (RAG) systems within the context of visually rich documents. Traditional RAG methodologies have experienced limitations in effectively engaging with the multimodal nature of these documents. This research introduces two innovative components: a new dataset named ViDoSeek and a robust framework named ViDoRAG, which collectively aim to bridge existing gaps in retrieval, comprehension, and reasoning when dealing with complex visual documents.

Core Contributions

1. ViDoSeek Dataset: The paper establishes ViDoSeek, a dataset tailored specifically for evaluating RAG performance on documents that require advanced multi-hop reasoning capabilities. This dataset provides an effective evaluation framework by offering queries with unique answers across a large corpus, thus better isolating retrieval and generation tasks from prior benchmarks.
2. ViDoRAG Framework: The proposed ViDoRAG model leverages a multi-agent, coarse-to-fine approach to optimize the retrieval-augmented generation process for visually rich documents. It incorporates a Multi-Modal Hybrid Retrieval mechanism founded on a Gaussian Mixture Model (GMM). This mechanism adeptly balances the integration of visual and textual features to refine query-specific retrieval.
3. Iterative Reasoning Agents: The paper presents the implementation of an iterative reasoning process, involving an interaction of specialized agents - seeker, inspector, and answer agents - to conduct efficient exploration, summarization, and reflection workflows.

Strong Numerical Results

The ViDoRAG framework demonstrates a notable improvement over existing RAG benchmarks, achieving over a 10% enhancement in performance on the ViDoSeek dataset. This significant advancement reinforces the model's capability to effectively blend textual and visual modalities and efficiently scale across test-time scenarios, suggesting potential adaptability in heterogeneous datasets.

Implications and Future Directions

The implications of ViDoRAG extend beyond theoretical improvements to practical applications in fields such as finance, education, and law, where documents are increasingly multimodal and rich in visual elements. The multi-agent architecture encourages the development of sophisticated AI systems that can autonomously interpret and reason across diverse document types.

Looking ahead, the scalability and adaptability of the ViDoRAG framework invite further exploration into its application to other document types and modalities. Additionally, future research could focus on decreasing the computational overhead inherent in a multi-agent system, optimizing reasoning efficiency, and developing more generalized models that maintain high performance across varied contexts.

In conclusion, this research marks a critical step forward in enhancing the capabilities of RAG systems to operate seamlessly within complex, visually enriched document environments, offering both a methodological framework and a performance benchmark that contribute substantively to the field of artificial intelligence.