ACE-GraphRAG: Agentic Context Engineering for Hierarchical GraphRAG
Hierarchical Graph Retrieval-Augmented Generation (GraphRAG) organizes corpus knowledge at multiple levels of granularity, yet fixed context construction may fail to translate these multi-resolution representations into a context suited to the current query. We identify this mismatch as the representation--inference gap. We propose Agentic Context Engineering for Hierarchical GraphRAG (ACE-GraphRAG), an inference-time context policy layer that supplements and adapts the initial context for generation. ACE-GraphRAG formulates context construction as a policy over gap-aware refinement, retrieval branches, and task-conditioned adaptation. Parallel Differential Retrieval acquires supplementar...
| Authors | Yongfeng Huang, Yuren Lai, Ruiying Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 August 2026 |
| Last updated at source | 4 August 2026 |
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