Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...

One of the greatest weaknesses of AI agents that read and understand vast amounts of enterprise data is "hallucination"—the generation of plausible-sounding but factually incorrect information. KAIST ...

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

The cloud-based agentic AI platform aims to help human researchers overcome resource constraints and complex data challenges ...

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

When applied thoughtfully, agentic AI has the potential to turn classrooms into environments where students actively explore complex systems rather than passively absorb information ...

Pinterest launches Ask Pinterest, an AI-powered shopping app using its Taste Graph to deliver personalized recommendations ...

The TestComplete enhancement will be especially beneficial for business-critical applications, including 3D applications such as CAD, canvas-based applications including Google Maps, virtualized ...

Why the Fortune 500 Companies Winning the AI Race Specialized Before They Scaled Eight in ten CEOs now say their role is at ...

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

Here's why revenue teams need a "Marketing Engineer" — a systems designer who orchestrates agentic AI workflows — and how to hire and structure the role.

The future enterprise will not be defined by a clean boundary between humans and machines. It will be defined by systems of ...