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 — ...

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 ...
InfoWorld

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 ...

New Alibaba AI framework skips loading every tool, cutting agent token use 99%

A new framework called SkillWeaver tackles AI agent tool routing by skipping full-library loading, cutting token use 99% on ...
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?

KAIST: "Improving Data Processing Accuracy and Speed, Key to Overcoming Barriers to Enterprise AI Adoption"

Artificial Intelligence (AI) agents based on Retrieval-Augmented Generation (RAG) technology are rapidly proliferating. RAG ...
SiliconANGLE

Diffbot boosts LLM accuracy by tapping into its vast Knowledge Graph of up-to-date information

Knowledge graph startup Diffbot Technologies Corp., which maintains one of the largest online knowledge indexes, is looking to tackle the problem of hallucinations in artificial intelligence chatbots ...
Searchenginejournal.com

Unlocking The Power Of LLM And Knowledge Graph (An Introduction)

We are in an exciting era where AI advancements are transforming professional practices. Since its release, GPT-3 has “assisted” professionals in the SEM field with their content-related tasks.