Knowledge Graph

Paper: Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review (Nagori et al., 2025) One‑line: The authors wrap a hybrid RAG pipeline (Neo4j GraphRAG + FAISS VectorRAG) inside an agent (Llama‑3.3‑70B) that decides per query which retriever to use, then instruction‑tunes generation (Mistral‑7B) and quantifies uncertainty via bootstrapped evaluation. It’s open‑source and genuinely useful. Why this paper matters (beyond research circles) Business pain: Knowledge workers drown in PDFs. Static “semantic search + summarize” tools miss citation structure and provenance; worse, they hallucinate under pressure. What’s new: Dynamic query routing between graph queries (Cypher over Neo4j) and semantic + keyword retrieval (FAISS + BM25 + rerank). Then DPO nudges the generator to prefer grounded answers. So what: For regulated sectors (healthcare, finance, legal), this is a pattern you can implement today for auditable reviews with traceable sources and tunable confidence bands. The blueprint (concrete, reproducible) Ingestion: Pull bibliometrics (DOI, title, abstract, year, authors, PDF URL, source) from PubMed, arXiv, Google Scholar. Deduplicate and filter by cosine similarity of TF‑IDF keywords (keep top‑quartile relevance). ...

In the age of Digital Twins and hyper-automated warehouses, simulations are everywhere—but insights are not. Discrete Event Simulations (DES) generate rich, micro-level data on logistics flows, delays, and resource utilization, yet interpreting these data remains painfully manual, fragile, and siloed. This paper from Quantiphi introduces a compelling solution: transforming raw simulation outputs into a Knowledge Graph (KG) and querying it via an LLM agent that mimics human investigative reasoning. It’s a shift from spreadsheet-style summaries to an interactive AI assistant that explains why something is slow, where the bottleneck is, and what needs attention. ...

Retrieval-Augmented Generation (RAG) has become the go-to technique for grounding large language models (LLMs) in external data. But as anyone building real-world RAG pipelines knows, there’s a growing tension between accuracy and cost. Existing graph-based RAG solutions promise richer semantics than vanilla vector stores, but suffer from two persistent issues: incomplete graphs and retrieval misalignment. The paper “CUE-RAG: Towards Accurate and Cost-Efficient Graph-Based RAG” proposes a structural rethinking. By integrating a multi-partite graph, hybrid extraction, and a query-driven iterative retriever, CUE-RAG achieves state-of-the-art accuracy while cutting indexing costs by up to 72.58% and even outperforming other methods without using any LLM tokens at all. ...