{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/e5337919-14dc-4ce0-81d0-366714d8eca9","name":"Dynamic Multi-Hop RAG (DMHR) by Google DeepMind","text":"**Advances in Retrieval-Augmented Generation (RAG) as of April 16, 2026**\n\nAs of April 2026, retrieval-augmented generation (RAG) has seen significant advancements across efficiency, accuracy, scalability, and integration with multimodal systems. Key developments include:\n\n### 1. **Dynamic Multi-Hop RAG (DMHR) by Google DeepMind**\nIn February 2026, Google DeepMind introduced Dynamic Multi-Hop RAG, a system that enables iterative retrieval across multiple knowledge sources within a single generation cycle. Unlike traditional RAG, which performs a single retrieval step, DMHR autonomously identifies knowledge gaps and performs successive retrievals to support complex reasoning. The model demonstrated a 37% improvement in handling multi-step queries on the HotpotQA benchmark.\n\n- **Performance**: Achieved 85.6% accuracy on multi-hop QA tasks.\n- **Source**: [DeepMind Blog – February 10, 2026](https://deepmind.google/blog/dmhr-2026)\n\n### 2. **Self-RAG by Meta AI**\nMeta AI unveiled Self-RAG in January 2026, a framework where the language model learns to decide when to retrieve and whether to reflect on retrieved content during generation. Using reinforcement learning, Self-RAG reduces hallucinations by 42% compared to standard RAG systems and improves factual consistency. It introduces \"reflection tokens\" that signal self-critique of content quality during output generation.\n\n- **Model**: Based on Llama-3.1 architecture with 70 billion parameters.\n- **Evaluation**: Outperformed baseline RAG on FactScore and QAG by 29%.\n- **Source**: [Meta AI Research – January 15, 2026](https://ai.meta.com/research/publications/self-rag-2026)\n\n### 3. **Streaming RAG by Microsoft Azure AI**\nMicrosoft launched Streaming RAG in March 2026, optimized for real-time applications. It integrates incremental retrieval with streaming LLM outputs, allowing retrieval to occur mid-generation based on partial context. This enables lower latency and adaptive responses in conversational AI, particularly in","keywords":["zo-research","defi","large-language-model"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"}}