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

The paper introduces DocTrace, a multi-agent retrieval-augmented generation (RAG) framework designed for long-document question answering (QA). It features a lightweight document structural tree index and hypergraph-structured working memory that is query-triggered and experience-guided, addressing limitations in knowledge organization and reasoning reuse. Experimental results demonstrate that DocTrace outperforms the baseline model ComoRAG by up to 8.85% in F1 and 4.40% in EM across multiple datasets while achieving a 53.32% reduction in computational cost, making it a significant advancement for practitioners dealing with long-document QA tasks.