arXiv:2606.07235v1 Announce Type: cross Abstract: Long, multimodal documents force retrieval-augmented systems to assemble answers from evidence fragmented across text, tables, and slides broken across cells in a long table, spread over multiple slides, or split between a figure and its discussion. Top-$k$ chunk retrieval treats each fragment independently and cannot represent how evidence connects. We introduce FLOWREADER, which reframes evidence assembly as a min-cost flow problem on a multimodal node graph: a single scoring vector $h$ controls source selection (via MMR), sink selection (via
The proliferation of long, multimodal documents in enterprises creates a growing need for advanced Q&A systems that can seamlessly integrate disparate information types for effective knowledge retrieval.
This development enhances the capability of AI systems to process and synthesize complex information from diverse sources, improving accuracy and reliability in decision-making and automation.
Current retrieval-augmented generation (RAG) systems are limited by fragment independence, but FLOWREADER's min-cost flow optimization allows for a more connected, contextual assembly of evidence for complex Q&A.
- · Enterprise AI providers
- · Knowledge management platforms
- · Law firms
- · Consulting firms
- · Traditional keyword-based search engines
- · Information workers performing manual data synthesis
More accurate and comprehensive answers from complex document sets become achievable through advanced AI.
This could lead to increased automation of information analysis tasks in sectors dealing with extensive data, such as finance and legal.
The development might accelerate the adoption of agentic AI systems capable of deep contextual understanding across various business functions.
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Read at arXiv cs.LG