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?
IEEE

A Topical Review of Quantum and Classical Machine Learning Approaches to Disaster Escape Routing Problems

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 ...
IEEE

Multi-Agent Reinforcement Learning With Spatial Structure Awareness for Topological Map-Based Path-Finding

Abstract: Efficient Multi-Agent Path Finding (MAPF) is pivotal for warehouse logistics. While existing learning-based methods primarily rely on computationally intensive grid-based representations, ...
GitHub

Let your AI agent query your Obsidian vault's knowledge graph directly

Your vault has a graph. But you can only stare at it. Obsidian's graph view is beautiful, but it can't answer questions — "Which notes have the most links?" "How many hops between these two concepts?" ...
GitHub

Guidance Graph Optimization for Lifelong Multi-Agent Path Finding

This repository is the official implementation of Guidance Graph Optimization for Lifelong Multi-Agent Path Finding, accepted to IJCAI 2024. The repository builds on top of the repository of ...