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specs/gnn_topology.yaml

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+# 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"