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

Google shipped two new specs weeks apart. Here's what OKF and ARD actually do, how they differ from LLMs.txt and MCP, and ...

Quantum computing gas turbine simulation moved forward July 14 as Quantinuum, Rolls-Royce, Riverlane, and EPCC signed a multi ...

Indianapolis-based Selflessly rebuilt its corporate giving platform around Phil, an AI assistant built for every ...

Deep learning variant calling has transformed genomic accuracy. Discover how DeepVariant works, outperforms classical tools, ...

Abstract: Many robotics applications benefit from being able to compute multiple geodesic paths in a given configuration space. Existing paradigm is to use topological path planning, which can compute ...

Instagram algorithm control is now in users' hands via the "Your Algorithm" feature, live across Feed, Reels, and Explore ...

Abstract: The pathfinding problem in a graph has been solved using several classical algorithms, notably Dijkstra’s and A* algorithms. However, most classical algorithms are most effective on static ...

We introduce the heat method for solving the single- or multiple-source shortest path problem on both flat and curved domains. A key insight is that distance computation can be split into two stages: ...

Agent observability, aka AgentOps, has emerged as a vital ecosystem of tools for keeping an eye on what AI agents and LLMs ...

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Gwen Shapira shares how teams are scaling AI features using PostgreSQL for mission-critical apps. She explains how to ...