ACE-GraphRAG: Agentic Context Engineering for Hierarchical GraphRAG

ACE-GraphRAG treats context construction in hierarchical GraphRAG as an inference-time policy, targeting what the paper calls the representation-inference gap between multi-resolution graph representations and query-specific generation needs. Its Parallel Differential Retrieval adds evidence through depth-oriented factual and breadth-oriented semantic branches, while provenance and abstraction levels are preserved during consolidation. Full-ACE applies a uniform policy within each task family; Adaptive-ACE selects policies per query using task and topology signals. The paper evaluates HotpotQA, 2WikiMultiHopQA, and four UltraDomain subsets for multi-hop QA and query-focused summarization. The abstract reports gains over evaluated RAG and GraphRAG baselines, with further Adaptive-ACE benefits on multi-hop QA and UltraDomain.