README.md

GNN Ruby Code Study

Systematic study of Graph Neural Network architectures for Ruby code complexity prediction and generation.

Key Findings

1. 5-layer GraphSAGE achieves MAE 4.018 (R^2 = 0.709) for complexity prediction - 16% better than 3-layer baseline
2. GNN autoencoders produce 0% valid Ruby across all tested architectures and loss functions
3. Teacher-forced GIN decoder achieves 7% validity - the only non-zero result in 35 experiments
4. Total cost: ~$4.20 on Vast.ai RTX 4090 instances

Contents

• paper.md - Full research paper
• results/experiments.json - All fleet experiment metrics
• results/autonomous_research.json - 18 baseline variance replicates
• experiments/ - Fleet YAML specifications for all 3 tracks
• specs/ - Ratiocinator ResearchSpec YAMLs

Source Code

• Model code: jubilant-palm-tree (branch: experiment/ratiocinator-gnn-study)
• Orchestrator: ratiocinator