Show HN: Graph-Oriented Generation – Beating RAG for Codebases by 89%
dchisholm125 Friday, March 06, 2026
LLMs are better at being the "mouth" than the "brain" and I can prove it mathematically. I built a deterministic graph engine that offloads reasoning from the LLM. It reduces token usage by 89% and makes a tiny 0.8B model trace enterprise execution paths flawlessly. Here is the white paper and the reproducible benchmark.
Summary
The article describes a novel approach to graph-oriented data generation, which involves using Transformers and graph neural networks to generate graph-structured data. The authors present a framework that can generate diverse and realistic graphs with specific properties, which could be useful for tasks like network analysis and machine learning.
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github.com