[2603.27922] GEAKG: Generative Executable Algorithm Knowledge Graphs

Computer Science > Artificial Intelligence arXiv:2603.27922 (cs) [Submitted on 30 Mar 2026] Title:GEAKG: Generative Executable Algorithm Knowledge Graphs Authors:Camilo Chacón Sartori, José H. García, Andrei Voicu Tomut, Christian Blum View a PDF of the paper titled GEAKG: Generative Executable Algorithm Knowledge Graphs, by Camilo Chac\'on Sartori and 3 other authors View PDF HTML (experimental) Abstract:In the context of algorithms for problem solving, procedural knowledge -- the know-how of algorithm design and operator composition -- remains implicit in code, lost between runs, and must be re-engineered for each new domain. Knowledge graphs (KGs) have proven effective for organizing declarative knowledge, yet current KG paradigms provide limited support for representing procedural knowledge as executable, learnable graph structures. We introduce \textit{Generative Executable Algorithm Knowledge Graphs} (GEAKG), a class of KGs whose nodes store executable operators, whose edges encode learned composition patterns, and whose traversal generates solutions. A GEAKG is \emph{generative} (topology and operators are synthesized by a Large Language Model), \emph{executable} (every node is runnable code), and \emph{transferable} (learned patterns generalize zero-shot across domains). The framework is domain-agnostic at the engine level: the same three-layer architecture and Ant Colony Optimization (ACO)-based learning engine can be instantiated across domains, parameterized by ...