Astronomers using computer simulations have investigated whether a class of star clusters nicknamed "cosmic wallflowers" ...

GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search ...

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

Part of the SD Times 100 2026 series. See the full SD Times 100 2026 list for every category and honoree. Every conversation ...

NUS researchers' MRAgent framework reduces LLM agent memory retrieval to 118K tokens per query — vs. 3.26M for LangMem — ...

Sub-headline: HUST researchers systematize SNA methods, building an evolutionary taxonomy based on graph representation ...

Most teams bolt a vector database to a graph database and then spend their lives keeping the two in sync. Two systems, two copies of the data, two failure modes, and a layer of glue code in between ...

In this Trader Talk TV episode, Travis O'Neil, vice president of product at GumGum, joins ExchangeWire COO Lindsay Rowntree at the legendary whiteboard to explain the Mindset Graph and relevancy ...

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a ...

One of the biggest problems with the Switch 2 isn’t availability or a looming price hike, but the platform’s online marketplace. What should be a highlight reel of Nintendo exclusives, outstanding ...

If you've ever taken an introductory astronomy class, you've probably seen the Hertzsprung-Russell (HR) diagram. This graph maps out the life cycle of stars by plotting their temperature against their ...

Abstract: Temporal Knowledge Graph Embedding (TKGE) vectorizes the elements of quadruples and uses vector computation to predict unobserved knowledge graph information from existing data. However, ...