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specs/gnn_generation.yaml

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+# Research Spec: GNN Code Generation Failure Analysis
+#
+# Systematic investigation of why GNNs fail at code generation.
+# Tests loss functions, decoder architectures, embedding dimensions.
+#
+# Launch:
+#   ratiocinator research specs/gnn_generation.yaml
+#
+# Requires pre-collated data (.pt files). Either stage via rsync
+# or generate on-instance from the JSONL files in the branch.
+
+# What to research
+topic: "Systematic analysis of why Graph Neural Networks fail at code generation from ASTs. Testing hypotheses: (1) MSE vs cross-entropy loss on node types, (2) different decoder GNN architectures (GCN/GAT/GIN/SAGE), (3) embedding dimension (64/128/256). Baseline: 0% syntactic validity with GAT decoder, MSE loss, 64D embeddings."
+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_generation_arm.sh
+
+# Infrastructure — decoder models ~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; pre-collation done on-instance
+
+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 — longer training per arm (~10-20 min)
+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"