arXiv (All)
open access
Graph Tokenization for Bridging Graphs and Transformers
The success of large pretrained Transformers is closely tied to tokenizers, which convert raw input into discrete symbols. Extending these models to graph-structured data remains a significant challenge. In this work, we introduce a graph tokenization framework that generates sequential representati
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
- Improving Parameter Utilization by Sharing Neural Experts Across Layers in Transformers#1036882
- The Status of Arguments in Abstract Argumentation Frameworks. A Tableaux Method#709036
- The X.500 String Representation of Standard Attribute Syntaxes#158832
- Entanglement of multi-qubit quantum graph states and studies structural properties of tripartite graphs with quantum computing#1063903
- CVE-2026-102270 — MEDIUM 4.4#1134924
Record · ID 986831
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