TAI #226: The AI Model Race Needs Better Tests
Also, Meta’s Muse Wonder, Claude Haiku 5.5, OpenAI’s Ultrafast tier, Mistral Large 4, Reflection Beam, and new rivals to Jev.
Towards AI17 min read
GraphRAG (Microsoft)
21 stories
BEACON-SP: Ontology-Grounded GraphRAG Framework for Clinical Suicide Risk Assessment
We present BEACON-SP, an ontology-grounded Graph Retrieval-Augmented Generation (GraphRAG) framework for clinician-facing decision support in behavioral health settings such as suicide prevention, where effective assessment requires integrating heterogeneous clinical, behavioral, social, and…
arXiv - cs.AI2 min read
Best Tools for Preventing AI Hallucinations in Production (2026)
An AI system can give a confident answer its source data doesn’t support. It can retrieve the wrong document and summarize it fluently, pick the...
Opencontroller by lyzr Blog13 min read
What Is a Context Graph? How It Differs From a Knowledge Graph, and How to Build One
Coworker AI explains the context graph: a knowledge graph plus decision traces and time, how it differs from a knowledge graph, and how to build one.
Coworker AI Blog30 min read
Vector Search Is Not Enough: The Blueprint of GraphRAG
Vector Search Is Not Enough: The Blueprint of GraphRAG Vector search tells you what is similar; GraphRAG tells you how it is connected. Retrieval-Augmented Generation (RAG) has emerged as an …
Towards AI11 min read
Why Vector Search Fails at Multi-Hop Reasoning: Building an Auditable GraphRAG Engine with Neo4j…
Learn why vector RAG fails at multi-hop reasoning and how combining Neo4j Knowledge Graphs with Gemini creates auditable enterprise AI.
Towards AI6 min read
When do AI agents actually matter? Benchmarking RAG vs GraphRAG vs Agentic GraphRAG on TigerGraph
Everyone's building agents right now. But a better question than "can an agent do this?" is "when does an agent actually beat something simpler?" For the TigerGraph Agentic GraphRAG Hackathon, I built three question-answering pipelines side by side to find out.
Dev.to AI3 min read
# When does an AI agent actually earn its cost? I measured it on 100 questions
Built for the TigerGraph Agentic GraphRAG Hackathon. Repo and dashboard linked at the end. RAG retrieves text. GraphRAG adds structure. Agentic GraphRAG lets a model plan its own investigation. Everyone assumes the third one is better — but by how much, and on which questions?
Dev.to AI5 min read
From Raw Data to Graph-Native AI
I’ve been thinking about a gap in the way we discuss graphs in AI. Most of the conversation starts after the graph already exists. We talk about graph neural networks, GraphRAG, graph agents, and graph foundation models.
O'Reilly Radar - AI/ML7 min read
GraphRAG with TypeSafe Jev: A System One Approach to Scalable Knowledge Graphs
How calibrated decision models can handle high-frequency graph decisions while LLMs remain focused on reasoning, synthesis, and open-ended generation. The post GraphRAG with TypeSafe Jev: A System One Approach to Scalable Knowledge Graphs appeared first on Towards Data Science.
Towards Data Science16 min read
Knowledge Graph vs Vector Search for Agent Memory: When to Add a Graph, and Why Time Matters
For agent memory, neither wins on its own: vector search finds past statements that mean something close to the current question, and a knowledge graph connects facts through the same person or company and tells which of two conflicting facts is current.
Maximem Synap Blog18 min read
What Is a Graph Database, and When Should You Use One?
A graph database stores data as nodes and relationships and answers questions by following those relationships instead of joining tables; use one when your main questions are about paths across connected data, and keep a relational database for records and reporting.
Maximem Synap Blog20 min read
What Is RAG? How Retrieval-Augmented Generation Works, and Where It Stops
RAG, short for retrieval-augmented generation, is a technique that lets a large language model answer from information it was never trained on: when a question arrives, the application searches a knowledge source for the most relevant passages, places them in the prompt next to the question, and…
Maximem Synap Blog16 min read
How to Build a GraphRAG System with Python, Neo4j and ServiceNow [Full Book]
Somewhere in your company's ServiceNow instance is the answer to the question an engineer asks at two in the morning: if this is broken, what else is about to break? Every fact needed to answer it has
freeCodeCamp — AI346 min read
History of Mind Mapping: From Porphyry to Buzan to AI (2026)
The real history of mind mapping, with sources. Porphyry never drew a tree, Leonardo's map has never been produced, and the best-controlled study found no benefit. Here is what actually happened, from 270 AD to GraphRAG.
Taskade Blog24 min read
Inside LinkedIn's cognitive memory agent for agentic personalization
Ryan is joined by Praveen Bodigutla, Principal AI Researcher at LinkedIn, to chat about the four-layer memory system his team built to give LinkedIn's hiring assistant a persistent, personalized state.
Stack Overflow Blog - AI29 min read
Graph engineering is missing a graph
Graph engineering stops at the diagram. The harder half is the context graph underneath: whether relations, vectors and memory share one consistency boundary.
SurrealDB Blog20 min read
Why Text-to-SQL AI Agents Hallucinate — and How Knowledge Graphs Fix It
AI agents can now answer questions about your company's data in plain English — but on real enterprise schemas, even frontier models got roughly one answer in five right at launch.
Scrollypedia
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