{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/f3382a41-cc2e-4324-a119-ae1c0ce9044b","name":"Advances in retrieval-augmented generation (RAG)","text":"## Key Findings\n- Recent Advances in Retrieval-Augmented Generation (RAG) as of April 15, 2026**\n- As of April 15, 2026, retrieval-augmented generation (RAG) has seen significant advancements across scalability, accuracy, and integration with multimodal systems. Key developments include:\n- 1. **Google DeepMind’s Adaptive RAG (AdaptRAG)**\n- In February 2026, Google DeepMind introduced AdaptRAG, a framework that dynamically adjusts retrieval strategies based on query complexity and context. It uses a meta-learning controller to decide whether to retrieve from dense vector indexes, knowledge graphs, or hybrid sources. Evaluation on the Natural Questions and HotpotQA benchmarks showed a 12–15% improvement in answer accuracy over standard RAG models.\n- Source: [https://deepmind.google/discover/papers/adaptive-rag-2026](https://deepmind.google/discover/papers/adaptive-rag-2026)*\n\n## Analysis\n2. **Meta’s Self-Correcting RAG (SC-RAG)**\n\nMeta announced SC-RAG in March 2026, a system that iteratively refines retrieved documents and generated responses using internal consistency checks and external verification modules. SC-RAG reduces hallucinations by up to 40% compared to prior RAG pipelines, according to internal benchmarks. It is now integrated into Meta’s Llama Assistants for enterprise use.\n\n*Source: [https://ai.meta.com/blog/sc-rag-llama-2026](https://ai.meta.com/blog/sc-rag-llama-2026)*\n\n## Sources\n- https://deepmind.google/discover/papers/adaptive-rag-2026\n- https://ai.meta.com/blog/sc-rag-llama-2026\n- https://www.microsoft.com/en-us/research/project/graphrag-plus\n- https://openai.com/research/fusion-retrieval-gpt5\n- https://github.com/InfuseAI/RAGFlow/releases/tag/v2.0\n- https://www.anthropic.com/news/multimodal-rag-claude-3-5\n\n## Implications\n- Evaluation on the Natural Questions and HotpotQA benchmarks showed a 12–15% improvement in answer accuracy over standard RAG models\n- SC-RAG reduces hallucinations by up to 40% compared to prior RAG pipelines, according to in","keywords":["zo-research"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"}}