Databricks built a RAG agent it says can handle every kind of enterprise search

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

How to revise your old content for AI search optimization

AI rewards clear answers and structured, retrievable content. Learn how to reformat, prioritize, and refine metadata for ...
IEEE

An improved genetic algorithm with variable neighbourhood search for flexible job-shop scheduling problem

Abstract: The Flexible Job Shop Scheduling Problem (FJSP), a classical NP-hard problem, requires the solution of two aspects of the complex problem, the first is to assign each operation to an ...

Rebuilding faces and identifying tattoos, AI joins the search for the missing in Mexico

For some years now, groups, universities, organizations and authorities have developed and implemented AI for data analysis, tattoo identification, facial recognition and to give a voice to the ...
IEEE

A Parallel Decoupling Self-planning Search-attack Algorithm for Multi-UAV with Reward Estimation

Abstract: This paper proposes a novel algorithm for multiple unmanned aerial vehicles (multi-UAV) cooperative search-attack missions operated in uncertain environments. A parallel decoupling ...

How to build a context-first AI search optimization strategy

AI-driven discovery depends on semantic depth and retrievable structure. Align language, taxonomy, and schema for modern search visibility.