arXiv (All)
open access
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 o
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- AutoGraphForge: Towards Automated Graph Theory Discovery#926397
- A Generic Framework for Engineering Graph Canonization Algorithms#632952
- GeoSkill:Experience-Driven Hierarchical Skill Learning with Collaborative Revision forGeospatialAgents#1071434
- From Interpretation to Compilation: A Compilation-Based Execution Engine for Semantic Operator Systems#440455
- From Execution to Capability: Scientific Experience Consolidation via Procedural Knowledge Synthesis#405706
Record · ID 1035442
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive