Datasets:
| # 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](https://github.com/timlawrenz/jubilant-palm-tree) (branch: `experiment/ratiocinator-gnn-study`) | |
| - Orchestrator: [ratiocinator](https://github.com/timlawrenz/ratiocinator) | |