Trace Only What You Need: Structure-Aware On-Demand Hypergraph Memory for Long-Document Question Answering

• URL: http://arxiv.org/abs/2606.10921v1
• Date: Tue, 09 Jun 2026 14:29:06 GMT
• Title: Trace Only What You Need: Structure-Aware On-Demand Hypergraph Memory for Long-Document Question Answering
• Abstract summary: Long-document question answering requires large language models to reason over evidence scattered across lengthy documents.<n>Existing structured RAG methods face three limitations: costly query-agnostic knowledge organization, insufficient use of original document structure, and no reuse of historical reasoning experience.<n>We propose DocTrace, a multi-agent RAG framework for long-document QA that supports query-triggered knowledge organization, document-structure-aware and experience-guided reasoning.
• Score: 11.17007249581283
• License: http://creativecommons.org/licenses/by/4.0/
• 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 address these limitations, we propose DocTrace, a multi-agent RAG framework for long-document QA that supports query-triggered knowledge organization, document-structure-aware and experience-guided reasoning. DocTrace preserves document hierarchy with a lightweight document structural tree index, constructs agent-shared hypergraph-structured working memory on demand during reasoning, and stores successful reasoning plans in graph-structured experience memory for future reuse, enabling adaptive exploration across related long-document questions. Experiments on four long-document QA datasets show that DocTrace achieves the best performance on three datasets, surpassing the strongest baseline, ComoRAG, by up to 8.85% in F1 and 4.40% in EM, while reducing the overall computational cost by 53.32%

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