arXiv:2607.05927v1 Announce Type: cross Abstract: Multimodal document retrieval aims to retrieve relevant pages while preserving both textual and visual content from the original document. However, existing benchmarks primarily evaluate simple lexical or semantic matching, and most methods encode pages independently. Consequently, they overlook the contextual information in the document required to resolve queries that aggregate information across multiple pages. In this paper, we introduce CMDR and CMDR-Bench, a new multimodal document retrieval task and benchmark that require modeling docume
The proliferation of multimodal AI models and the increasing complexity of information retrieval tasks necessitate more sophisticated benchmarks beyond simple lexical matching.
Improved multimodal document retrieval is critical for advanced AI applications requiring deep contextual understanding across diverse data formats, impacting research and enterprise intelligence.
The focus shifts from independent page encoding to methods that leverage contextual information within and across document pages for more accurate and comprehensive information retrieval.
- · AI researchers focusing on multimodal understanding
- · Generative AI companies
- · Enterprise search solutions
- · Knowledge management platforms
- · Traditional semantic search engines
- · AI models lacking contextual understanding
- · Document management systems relying on keyword matching
More accurate and nuanced information extraction from complex documents will become possible.
This can lead to more sophisticated AI agents capable of deeper document interpretation and synthesis.
Accelerated breakthroughs in fields requiring contextual analysis of large document corpuses, such as legal tech or scientific discovery, could emerge.
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Read at arXiv cs.AI