arXiv:2605.29606v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) for document-based Open-domain Question Answering (ODQA) on large-scale industrial corpora faces two critical bottlenecks: routing failure in locating the correct document and evidence fragmentation in integrating scattered information. Existing approaches relying on flat text chunks or page-level images inherently struggle to (i) precisely pinpoint the target document among thousands of candidates and (ii) organically connect multimodal evidence, such as tables and figures, within a limited token budget. To a
The proliferation of RAG systems and multimodal data necessitates more sophisticated retrieval mechanisms to overcome current bottlenecks in efficiency and accuracy.
Improved multimodal retrieval directly enhances the performance, trustworthiness, and scalability of AI systems, particularly in critical applications like open-domain question answering.
AI systems will be able to more accurately and efficiently parse complex, multimodal documents, leading to better factual grounding and reduced AI 'hallucinations'.
- · AI developers
- · Enterprises with large document corpora
- · Knowledge management platforms
- · Generative AI users
- · AI systems relying on flat retrieval
- · Manual data compilation tasks
- · Inefficient document search solutions
More reliable and capable AI assistants for complex information retrieval.
Acceleration of AI adoption in sectors requiring deep document understanding, such as legal, medical, and scientific research.
Enhanced AI agents capable of autonomously synthesizing insights from vast, diverse information sources, impacting white-collar work automation.
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Read at arXiv cs.AI