Trace Only What You Need: Structure-Aware On-Demand Hypergraph Memory for Long-Document Question Answering
arXiv:2606.10921v1 Announce Type: new Abstract: Long-document question answering (QA) requires large language models (LLMs) to reason over evidence scattered across lengthy documents, where answers often depend on event order, section-level context, and cross-part evidence connections. Although retrieval-augmented generation (RAG) reduces the input context by retrieving relevant evidence, existing structured RAG methods still face three limitations: costly query-agnostic knowledge organization, insufficient use of original document structure, and no reuse of historical reasoning experience. To
The continuous drive to improve large language model performance, especially in practical applications like long-document QA, necessitates novel architectural and memory solutions.
Improving long-document QA directly impacts the efficiency and utility of AI agents and knowledge work automation, making complex information accessible and actionable.
This research proposes a more efficient and effective method for LLMs to process and reason over extensive documents, potentially leading to more accurate and reliable AI outputs.
- · AI software developers
- · Enterprises with large document bases
- · AI agents sector
- · Knowledge management platforms
- · Inefficient RAG methods
- · Manual data extraction processes
More capable and accurate AI systems for complex information retrieval and analysis will emerge.
This will accelerate the deployment and adoption of AI assistants and autonomous agents across various industries.
Increased efficiency in information processing could lead to significant productivity gains and redefine professional roles reliant on document analysis.
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Read at arXiv cs.CL