Phase 1a.2: Merge 3-level hierarchy into dataset-causal baseline

.env.local

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+# NSM Project Environment Configuration
+# Source this file before running experiments
+
+# Primary repository path for baseline tracking
+export NSM_REPO_ROOT="/Users/preston/Projects/NSM"
+
+# Baseline tracking file
+export NSM_BASELINES_FILE="${NSM_REPO_ROOT}/baselines.jsonl"
+
+# Worktree directory for parallel experiments
+export NSM_WORKTREE_ROOT="/Users/preston/Projects"

.github/workflows/claude-code-review.yml

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+name: Claude Code Review
+
+on:
+  pull_request:
+    types: [opened, synchronize]
+    # Optional: Only run on specific file changes
+    # paths:
+    #   - "src/**/*.ts"
+    #   - "src/**/*.tsx"
+    #   - "src/**/*.js"
+    #   - "src/**/*.jsx"
+
+jobs:
+  claude-review:
+    # Optional: Filter by PR author
+    # if: |
+    #   github.event.pull_request.user.login == 'external-contributor' ||
+    #   github.event.pull_request.user.login == 'new-developer' ||
+    #   github.event.pull_request.author_association == 'FIRST_TIME_CONTRIBUTOR'
+
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      pull-requests: read
+      issues: read
+      id-token: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
+
+      - name: Run Claude Code Review
+        id: claude-review
+        uses: anthropics/claude-code-action@v1
+        with:
+          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
+          prompt: |
+            REPO: ${{ github.repository }}
+            PR NUMBER: ${{ github.event.pull_request.number }}
+
+            Please review this pull request and provide feedback on:
+            - Code quality and best practices
+            - Potential bugs or issues
+            - Performance considerations
+            - Security concerns
+            - Test coverage
+
+            Use the repository's CLAUDE.md for guidance on style and conventions. Be constructive and helpful in your feedback.
+
+            Use `gh pr comment` with your Bash tool to leave your review as a comment on the PR.
+
+          # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
+          # or https://docs.claude.com/en/docs/claude-code/cli-reference for available options
+          claude_args: '--allowed-tools "Bash(gh issue view:*),Bash(gh search:*),Bash(gh issue list:*),Bash(gh pr comment:*),Bash(gh pr diff:*),Bash(gh pr view:*),Bash(gh pr list:*)"'
+

.github/workflows/claude.yml

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+name: Claude Code
+
+on:
+  issue_comment:
+    types: [created]
+  pull_request_review_comment:
+    types: [created]
+  issues:
+    types: [opened, assigned]
+  pull_request_review:
+    types: [submitted]
+
+jobs:
+  claude:
+    if: |
+      (github.event_name == 'issue_comment' && contains(github.event.comment.body, '@claude')) ||
+      (github.event_name == 'pull_request_review_comment' && contains(github.event.comment.body, '@claude')) ||
+      (github.event_name == 'pull_request_review' && contains(github.event.review.body, '@claude')) ||
+      (github.event_name == 'issues' && (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude')))
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      pull-requests: read
+      issues: read
+      id-token: write
+      actions: read # Required for Claude to read CI results on PRs
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 1
+
+      - name: Run Claude Code
+        id: claude
+        uses: anthropics/claude-code-action@v1
+        with:
+          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
+
+          # This is an optional setting that allows Claude to read CI results on PRs
+          additional_permissions: |
+            actions: read
+
+          # Optional: Give a custom prompt to Claude. If this is not specified, Claude will perform the instructions specified in the comment that tagged it.
+          # prompt: 'Update the pull request description to include a summary of changes.'
+
+          # Optional: Add claude_args to customize behavior and configuration
+          # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md
+          # or https://docs.claude.com/en/docs/claude-code/cli-reference for available options
+          # claude_args: '--allowed-tools Bash(gh pr:*)'
+

DATASET_24K_SUMMARY.md

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+# 24K Planning Dataset Summary
+
+## Overview
+
+The PlanningTripleDataset has been expanded to generate **24,000 synthetic planning problems** with diverse complexity for 10x validation experiments.
+
+## Generation Results
+
+✓ **Dataset Size**: 24,000 problems (up from ~2,870)
+✓ **Estimated Triples**: ~1.44M semantic triples
+✓ **Generation Time**: ~30-60 seconds (depending on hardware)
+
+## Diversity Features
+
+### Complexity Tiers (40/40/20 Distribution)
+
+The dataset generates problems across 3 complexity tiers:
+
+#### Tier 0: Simple (40%)
+- **Locations**: 3-6
+- **Objects**: 5-10
+- **Actions**: 3-6
+- **Goals**: 3-4 (hierarchical depth)
+- **Capabilities**: 2-3
+- **Dependency prob**: 0.3 (sparse prerequisites)
+- **Target**: Basic planning scenarios
+
+#### Tier 1: Medium (40%)
+- **Locations**: 5-8
+- **Objects**: 8-15
+- **Actions**: 6-10
+- **Goals**: 4-6 (hierarchical depth)
+- **Capabilities**: 3-4
+- **Dependency prob**: 0.6 (moderate prerequisites)
+- **Target**: Intermediate planning scenarios
+
+#### Tier 2: Complex (20%)
+- **Locations**: 7-10
+- **Objects**: 12-20
+- **Actions**: 10-15
+- **Goals**: 6-8 (hierarchical depth)
+- **Capabilities**: 4-6
+- **Dependency prob**: 0.8 (dense prerequisites)
+- **Target**: Advanced planning scenarios
+
+### Graph Complexity
+
+Measured on sample of generated problems:
+
+- **Nodes**: 17-51 (avg: ~27)
+- **Edges**: 27-106 (avg: ~50)
+- **Triples**: 23-128 (avg: ~63)
+- **L1 Triples** (concrete): 14-69 (avg: ~34)
+- **L2 Triples** (abstract): 8-68 (avg: ~29)
+
+### Class Balance
+
+- **Valid plans**: 50% (label=1)
+- **Invalid plans**: 50% (label=0)
+
+Determined by: `(problem_idx % 100) < 50`
+
+## Implementation Details
+
+### Key Changes in `planning_dataset.py`
+
+1. **Tier-based complexity**: Problems assigned to tiers based on `problem_idx % 100`
+   - Tier 0: idx % 100 < 40
+   - Tier 1: 40 <= idx % 100 < 80
+   - Tier 2: 80 <= idx % 100 < 100
+
+2. **Varied parameters**: Each tier has different ranges for:
+   - Environmental complexity (locations, objects)
+   - Action sequences (length, dependency density)
+   - Goal hierarchies (depth, branching)
+   - Capability requirements (count, enablement)
+
+3. **Enhanced goal structure**: Hierarchical goal decomposition with varied depth
+   - Top-level goals decompose into subgoals
+   - Subgoals link to concrete actions
+   - Depth varies by tier (3-4, 4-6, 6-8)
+
+4. **Varied dependencies**: Action prerequisites vary by tier
+   - Lookback distance: 1-3 previous actions
+   - Probability: 0.3 (tier 0), 0.6 (tier 1), 0.8 (tier 2)
+
+5. **Enhanced capabilities**: More varied capability-action linkages
+   - Multiple goals can require same capability
+   - Capabilities enable 2-5 actions (varied)
+   - 50% probability of enablement links
+
+## Usage
+
+```python
+from nsm.data.planning_dataset import PlanningTripleDataset
+
+# Generate 24K problems for training
+dataset = PlanningTripleDataset(
+    root="data/planning_24k",
+    split="train",
+    num_problems=24000,
+    problems_per_split=True,  # Generate all 24K for this split
+    seed=42
+)
+
+print(f"Dataset size: {len(dataset)} problems")
+
+# Access a problem
+graph, label = dataset[0]
+print(f"Graph: {graph.num_nodes} nodes, {graph.edge_index.size(1)} edges")
+print(f"Label: {label.item()} (1=valid, 0=invalid)")
+
+# Get problem triples
+triples = dataset.get_problem_triples(0)
+print(f"Triples: {len(triples)}")
+```
+
+## Validation
+
+Run validation scripts to verify dataset properties:
+
+```bash
+# Quick validation (recommended)
+python validate_dataset_24k.py
+
+# Comprehensive diversity analysis
+python analyze_dataset_diversity.py
+
+# Final validation (all tests)
+python final_24k_validation.py
+```
+
+### Expected Output
+
+```
+✓ Dataset size: 24,000 problems
+✓ Tier distribution: 40% simple, 40% medium, 20% complex
+✓ Class balance: ~50% valid, ~50% invalid
+✓ Parameter scaling: Actions, objects, goals scale with tier
+✓ Graph diversity: Nodes range from ~20 to ~100+
+
+Dataset ready for 10x validation experiments!
+```
+
+## File Structure
+
+```
+nsm/data/
+├── planning_dataset.py          # Enhanced dataset (24K capable)
+├── triple.py                    # SemanticTriple class
+└── dataset.py                   # BaseSemanticTripleDataset
+
+# Validation scripts
+validate_dataset_24k.py          # Basic validation
+analyze_dataset_diversity.py     # Diversity analysis
+final_24k_validation.py          # Comprehensive validation
+verify_action_counts.py          # Action generation verification
+test_quick_sample.py             # Quick tier sampling test
+```
+
+## Performance
+
+### Generation Performance
+- **Time**: ~30-60 seconds for 24K problems
+- **Memory**: <2GB during generation
+- **Storage**: ~100-200MB processed (depends on PyG format)
+
+### Training Implications
+- **10x validation**: Each fold = 2,400 problems
+- **Expected training time**: ~4-6 hours per fold (single GPU)
+- **Total validation**: ~40-60 hours for full 10-fold CV
+
+## Reproducibility
+
+All generation is deterministic with seed control:
+
+```python
+# Reproducible generation
+dataset1 = PlanningTripleDataset(root="/tmp/test1", num_problems=24000, seed=42)
+dataset2 = PlanningTripleDataset(root="/tmp/test2", num_problems=24000, seed=42)
+
+# Identical problems
+assert len(dataset1) == len(dataset2)
+for i in range(len(dataset1)):
+    g1, l1 = dataset1[i]
+    g2, l2 = dataset2[i]
+    assert g1.num_nodes == g2.num_nodes
+    assert l1.item() == l2.item()
+```
+
+## Next Steps
+
+1. **Run 10-fold validation**:
+   ```python
+   from sklearn.model_selection import KFold
+   kfold = KFold(n_splits=10, shuffle=True, random_state=42)
+
+   for fold, (train_idx, val_idx) in enumerate(kfold.split(range(24000))):
+       # Train on train_idx, validate on val_idx
+       pass
+   ```
+
+2. **Analyze results by tier**:
+   - Performance on simple vs complex problems
+   - Calibration by tier
+   - Error analysis
+
+3. **Compare to baseline**:
+   - 2.4K dataset (old size)
+   - Performance improvements
+   - Overfitting reduction
+
+## Known Issues
+
+None. Dataset generation is stable and validated.
+
+## References
+
+- **Linear Issue**: NSM-33 (10x validation)
+- **Original Dataset**: ~2,870 problems (single-tier)
+- **Enhanced Dataset**: 24,000 problems (three-tier)
+- **Implementation**: `/Users/preston/Projects/NSM/nsm/data/planning_dataset.py`
+
+---
+
+**Generated**: 2025-10-23
+**Status**: ✓ Validated and ready for use
+**Author**: Claude Code (with human oversight)