5arXiv cs.CL (Computation and Language)·
RAGU: Open-source GraphRAG engine with compact 7B extractor model outperforming 32B baseline
RAGU is a new open-source modular GraphRAG engine that separates knowledge graph extraction from consolidation, using two-stage typed extraction, DBSCAN-backed deduplication, LLM summarization, and Leiden community detection. The authors train Meno-Lite-0.1, a 7B model specialized for language skills, which outperforms Qwen2.5-32B on knowledge-graph construction by 12.5% relative harmonic mean while matching it on English GraphRAG tasks. On GraphRAG-Bench (Medical), RAGU achieves evidence recall up to 0.84 versus ≤0.76 for competing systems. The system is MIT-licensed, pip-installable, and runs on a single GPU.
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A new arXiv preprint introduces a systematic evaluation framework comparing nine standardized RAG scenarios across regular RAG, GraphRAG, Modular RAG, and Agentic RAG on semi-structured knowledge bases. The authors propose a novel context engineering method that reduces token usage by 19–53% for GraphRAG and Agentic RAG by addressing context/memory overflow. A key finding is a 'retrieval-generation gap' where expanded retrieval does not proportionally improve generation quality, suggesting retrieval-oriented metrics overstate the benefits of advanced retrieval. The work targets practitioners building production RAG systems and provides data-driven guidance on when to use each variant.
Microsoft GraphRAG trending on GitHub
Microsoft's GraphRAG repository, a modular graph-based Retrieval-Augmented Generation system implemented in Python, is trending on GitHub with 34,340 total stars and 33 new stars today. GraphRAG structures knowledge as a graph to improve retrieval quality over flat vector search. The continued traction signals ongoing practitioner interest in graph-augmented retrieval approaches.
UMG-RAG: Training-free hybrid retrieval with uncertainty-aware granularity fusion for long-document RAG
Researchers propose Uncertainty-aware Multi-Granularity RAG (UMG-RAG), a training-free hybrid retrieval framework that addresses the tension between large and fine-grained retrieval chunks in RAG pipelines. The system converts dense and sparse retriever scores across multiple chunk granularities into evidence distributions, estimates reliability via entropy, and fuses candidates using query-specific confidence signals. A variant called UMGP-RAG uses fine-grained hits to locate evidence while returning broader parent chunks for coherence. Experiments on QA benchmarks show improved generation quality with no changes to the underlying retriever or generator.
RAGFlow open-source RAG engine with agent capabilities trending on GitHub
RAGFlow is an open-source Retrieval-Augmented Generation engine that combines RAG with agent capabilities, positioned as a context layer for LLMs. The project has accumulated over 83,000 GitHub stars with 111 new stars today, indicating sustained community interest. It is maintained by Infiniflow and represents a notable open-source tooling option in the RAG/agent ecosystem.
HKVM-RAG: Hypergraph key-value separation improves multi-hop retrieval-augmented generation
A new arXiv preprint introduces HKVM-RAG, an evidence-organization layer for multi-hop RAG that uses weighted hyperedges as retrieval keys while retaining passage text as answer values. Under a fixed-substrate protocol controlling for tuple cache, reader, and evaluation budget, the hypergraph key-value approach improves over KG-PPR by +3.4 F1 on 2WikiMultiHopQA and +3.6 F1 on MuSiQue. A dense-aware controller combining frozen ColBERTv2 with HKVM features reaches 88.8, 65.1, and 85.8 F1 on three benchmarks, outperforming ColBERTv2 alone by 5–11 F1 points. The work positions hypergraph organization as a reusable evidence-control mechanism rather than a dense-retrieval replacement.
HippoRAG: RAG framework combining knowledge graphs and Personalized PageRank for continuous knowledge integration
HippoRAG is an open-source RAG framework published at NeurIPS 2024 by the OSU NLP Group that draws on models of human long-term memory to enable LLMs to continuously integrate knowledge across external documents. It combines retrieval-augmented generation with knowledge graphs and Personalized PageRank to improve multi-hop and associative retrieval. The repository has accumulated 3,742 GitHub stars with ongoing community traction.
Anthropic introduces Contextual Retrieval to reduce RAG retrieval failures by up to 67%
Anthropic published a technical method called Contextual Retrieval that combines Contextual Embeddings and Contextual BM25 to address the context-loss problem in traditional RAG pipelines. The approach prepends chunk-level context before encoding, reducing failed retrievals by 49% standalone and 67% when combined with reranking. The post also highlights prompt caching as a simpler alternative for knowledge bases under 200K tokens, and provides a cookbook for deployment with Claude.
Empirical evaluation of RAG metrics across four libraries against human judgments
A preprint from arXiv reports an empirical study comparing RAG evaluation metrics from four libraries—Ragas, DeepEval, RAGChecker, and Opik—against human annotator scores and standard metrics like recall on a business question-answering dataset. The study conducts correlation analysis between automated metrics and human evaluators, finding and documenting limitations of current RAG evaluation methodology. The paper is an English translation of work originally presented at the French-language EvalLLM workshop.