GraphRAG: What entity-first retrieval means for SEO

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

The Race to Build AI’s Context Layer Is Really About Meaning

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

New agentic memory framework uses 118K tokens per query. LangMem burns through 3.26M.

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

Microsoft unveils Memora to tackle AI agents’ memory problem

The research project promises more efficient long-term recall by organizing knowledge around abstractions and cue-based ...
EurekAlert!

Why GNSS-R soil moisture retrieval has relied on reference products—and how a physics-based approach is changing that

For decades, global soil moisture monitoring from space has depended on reference datasets. Satellite observations, while indispensable, are rarely used alone; instead, their retrieval algorithms are ...
VentureBeat

The retrieval rebuild: Why hybrid retrieval intent tripled as enterprise RAG programs hit the scale wall

Something shifted in enterprise RAG in Q1 2026. VB Pulse data spanning January through March tells a consistent story: the market stopped adding retrieval layers and started fixing the ones it already ...