Structure-Aware Quantized Retrieval for Long-Document Question Answering (2026.findings-acl)
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| Challenge: | Traditional long-document QA/RAG pipelines suffer from context fragmentation . Graph Neural Networks (GNNs) can encode hierarchy, but deep GNNs tend to over-smooth representations. |
| Approach: | They propose a framework that aligns hierarchical graph representations with a universal token vocabulary and integrates explicit structure into retrieval. |
| Outcome: | The proposed framework captures universal hierarchical patterns rather than overfitting to specific layouts. |
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