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README.md

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+# 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)