graphrag

该论文提出 GraphRAG-LTM 分层威胁建模框架，测试三种攻击者访问层级（黑盒查询、白盒查询、图感知白盒），发现检索排序操纵与关键三元组删除能显著削弱 GraphRAG 的答案质量与支撑事实检索。

作者分享对 GraphRAG 的兴趣，并尝试构建一个轻量本体论、无需向量搜索的 Jev-based GraphRAG 变体。

Mosaic is a training-free framework for query-aware exploration in GraphRAG that adapts retrieval policies based on query requirements, showing significant improvements in answer correctness and efficiency on standard benchmarks.

A technical post explaining why RAG fails for multi-hop queries and presenting a 9-step roadmap for building context graphs, where entities are nodes and relationships are edges, with a minimal 150-line engine.

This paper presents a triple-robustness analysis of RAG for multi-hop traceability, varying embedder, corpus, and judge across thousands of runs. It finds that over-citation is architecturally universal in GraphRAG but its faithfulness consequences are corpus-conditional, and LLM-as-judge faithfulness is fragile across retrieval states.

The article reviews 16 Agent Memory papers from the past two weeks, pointing out that the trend has shifted from static GraphRAG retrieval to self-evolving, multi-signal routing, cognitive models, and low-cost memory systems, with per-paper overviews and links included.

The author introduces ARGO, an open-source Agent Loop framework for Cursor, GitHub Copilot, and OpenCode that combines an ArchiMate-structured intent graph with GraphRAG retrieval to make AI coding delivery traceable. It separates intent, architecture, and implementation responsibilities, with two-level acceptance tests to avoid treating every failure as a code bug.

The author built a significantly cheaper alternative to GraphRAG for querying knowledge graphs using an agent, making it more accessible for various applications.

ColGraphRAG replaces single-vector bi-encoder similarity with late-interaction MaxSim scoring for ranking graph-linked image candidates in multimodal GraphRAG, improving retrieval and QA accuracy on MultimodalQA.

Yohei Nakajima announces his first arXiv paper on event-sourced reactive graphs for auditable, forkable agentic systems, connecting graphRAG, DAG workflows, and log/trace graphs.

RAGU is an open-source multi-step GraphRAG engine that uses a compact 7B fine-tuned LLM (Meno-Lite-0.1) to achieve high-quality knowledge graph construction at a fraction of the cost of larger models, outperforming larger systems on benchmarks.

A developer reflects on when GraphRAG is preferable to standard RAG for AI agents, noting it becomes valuable for multi-hop reasoning and relationship understanding, but adds significant complexity.

This article provides a practical guide to building a GraphRAG system using LangExtract, Neo4j, Qdrant, and Ollama, combining entity extraction, knowledge graphs, and vector search for context-aware retrieval.

Introduces Adaptive-masking for Graph Embedding (AGE), a Transformer-based self-supervised learning method that addresses latent feature misalignment between graph and text representations for LLMs in GraphRAG tasks by focusing on predicting non-key nodes.

A modular, high-performance Rust implementation of GraphRAG (Graph-based Retrieval Augmented Generation) with support for WebGPU acceleration and three deployment architectures: server-only, WASM-only (client-side), and hybrid.

IONS is an open-source approach to AI memory and reasoning that uses a graph of evidence-backed claims called Cognitive Building Blocks (CBBs) to store knowledge outside model weights, making reasoning inspectable.

A detailed technical query about building a local document RAG system covering storage, ingestion, query, and highlighting, seeking advice on vector databases, GraphRAG feasibility, and document highlighting implementations.

Hyper-Extract is an open-source framework that converts messy documents into typed knowledge structures, supporting multiple graph architectures like GraphRAG, LightRAG, and KG-Gen, with 10+ extraction engines and 80+ YAML templates for various domains.

An open-source full-stack pipeline that constructs a Knowledge Graph from raw text, uses hybrid search (dense + sparse + graph traversal) to solve multi-hop reasoning problems in LLMs, and re-ranks results with Reciprocal Rank Fusion and a Cross-Encoder.

A detailed guide on optimizing knowledge graph ingestion for AI agents, presenting a five-step pipeline (extraction, resolution, embedding, deduplication, routing) to prevent graph corruption and improve retrieval quality.