RAG for AI-Generated Content
A survey that extends RAG beyond text, showing how retrieval augmentation is being applied across code, image, audio, video, and 3D generation.
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Cross-modal taxonomy: Organizes RAG systems by how the retriever augments the generator (query, context, knowledge injection) across modalities, unifying methods that previously appeared disconnected.
Problem coverage: Maps which AIGC pain points each RAG pattern addresses - knowledge updates, long-tail data, leakage mitigation, and compute cost.
Enhancement catalog: Catalogues concrete techniques (re-rankers, multi-hop retrieval, modality-specific retrievers) and the tasks where each pays off.
Resources: Ships with benchmarks and a GitHub repo of 353 referenced papers, giving practitioners a structured entry point into this fast-moving area.