Knowledge Graph Enhanced Memory-Augmented Retrieval for Long Context Modeling
Long-context language modeling requires not only extending context windows but maintaining coherent understanding of entity states and relationships across thousands of tokens -- a challenge that semantic similarity alone cannot address. KGERMAR addresses this by constructing dynamic, context-specific knowledge graphs from input text during inference, enabling domain-adaptive retrieval that leverages both semantic similarity and explicit entity relationships. The framework performs real-time entity and relation extraction to build contextual knowledge graphs, then integrates graph-structural embeddings with textual semantics through a multi-component memory architecture. Three memory bank...
| Authors | Ghadir Alselwi, Basem Suleiman, Hao Xue et al. |
|---|---|
| Primary category | cs.IR |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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