Transferable knowledge graphs with executable learned operators for algorithm design
Procedural knowledge in algorithm design is embedded in source code and rebuilt for each new domain. We introduce Generative Executable Algorithm Knowledge Graphs (GEAKG), a representation in which this knowledge is stored as a generative, executable, transferable graph: typed nodes hold validated operators, edges encode admissible compositions, and learned edge weights record effective sequences. The same engine instantiates the structure across domains by changing only a role ontology (RoleSchema) and a binding. We study GEAKG as a representation mechanism rather than a state-of-the-art optimizer, asking what transfers and when. Layer ablations localize transfer by granularity: within a...
| Authors | Camilo Chacón Sartori, José H. García, Andrei Voicu Tomut et al. |
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| Primary category | cs.AI |
| Published | 30 March 2026 |
| Last updated at source | 18 September 2026 |
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