Continual Graph Retrieval-Augmented Generation under Corpus Expansion via Path Memory

GraphRAG supports multi-hop reasoning for complex tasks, but becomes less reliable as both documents and queries evolve over time. Corpus expansion can weaken previously useful evidence paths while introducing new evidence that remains undiscovered, and evolving queries further shift graph exploration. These dynamics challenge both retention and uptake of supporting evidence, characterized by evidence path dilution. We propose EviWeaver, a non-parametric continual framework that maintains and refreshes path memory from prior graph exploration. EviWeaver preserves useful evidence paths and incorporates newly relevant support entirely at retrieval time without retraining. Experiments across three multi-hop question answering benchmarks show that EviWeaver improves retrieval robustness and answer quality under continual corpus and query evolution.