Datasets:
| # Research Spec: GNN Decoder Topology — Track 4 | |
| # | |
| # Tests tree-aware decoding: giving the decoder the actual AST edge structure | |
| # instead of sequential chain edges. Three modes: | |
| # - chain: Legacy baseline (0→1→2→…) | |
| # - teacher_forced: Ground-truth AST edges during GNN message passing | |
| # - iterative: Two-pass: chain→predict parents→rebuild tree→refine | |
| # | |
| # The hypothesis: the decoder's GNN never sees tree topology, so it cannot | |
| # learn structure-sensitive generation. Providing real edges should improve | |
| # parent prediction accuracy and node-type diversity. | |
| # | |
| # Launch: | |
| # ratiocinator research specs/gnn_topology.yaml | |
| # What to research | |
| topic: "Decoder topology for GNN code generation: does giving the decoder the real AST tree structure (instead of sequential chain edges) improve reconstruction? Compare chain baseline, teacher-forced ground-truth edges, and iterative predict-then-refine. Cross with GAT/GCN/GIN decoder architectures and improved/comprehensive loss functions." | |
| goal_metric: syntactic_validity_pct | |
| maximize: true | |
| # Target codebase | |
| repo_url: https://github.com/timlawrenz/jubilant-palm-tree.git | |
| repo_branch: experiment/ratiocinator-gnn-study | |
| runner_script: scripts/run_topology_arm.sh | |
| # Infrastructure — ~850K params, moderate training | |
| hardware: | |
| gpu: "RTX 4090" | |
| num_gpus: 1 | |
| min_cpu_ram_gb: 32 | |
| min_inet_down: 1000.0 | |
| min_cuda_version: 12.0 | |
| max_dph: 0.40 | |
| disk_gb: 50.0 | |
| image: pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime | |
| data: | |
| source: none # Dataset is in the repo branch | |
| deps: | |
| pre_install: | |
| - "apt-get update -qq && apt-get install -y -qq git-lfs > /dev/null 2>&1 || true" | |
| - "cd /workspace/experiment && git lfs install && git lfs pull" | |
| - "pip install torch-geometric torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.7.0+cu128.html" | |
| - "pip install pandas tqdm sentence-transformers nltk scikit-learn numpy" | |
| requirements: requirements.txt | |
| exclude_from_requirements: | |
| - torch | |
| - torchvision | |
| - torch_geometric | |
| verify: "python -c \"import torch_geometric; print(f'PyG {torch_geometric.__version__}')\"" | |
| metrics: | |
| protocol: json_line | |
| json_prefix: "METRICS:" | |
| # Budget — ~9 arms (3 edge modes × 3 conv types), ~10 min each | |
| max_iterations: 2 | |
| max_dollars: 15.00 | |
| train_timeout_s: 2400 | |
| download_timeout_s: 600 | |
| # Output | |
| paper_title: "What Graph Neural Networks Can and Cannot Learn About Code: A Systematic Empirical Study on Ruby AST Analysis" | |