arXiv Artificial Intelligence

Transferable knowledge graphs with executable learned operators for algorithm design

1 hour ago 21.09.2026 07:00 👁 1 views
Transferable knowledge graphs with executable learned operators for algorithm design

Quick summary

arXiv:2603.27922v2 Announce Type: replace Abstract: 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 represen

Key takeaways

  • arXiv:2603.27922v2 Announce Type: replace Abstract: 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.

Why it matters

“Transferable knowledge graphs with executable learned operators for algorithm design” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗