Add CLAUDE.md and LaTeX paper, remove old papers directory

- Add CLAUDE.md with project guidance for Claude Code
- Add LaTeX/ with paper and figure generation scripts
- Remove papers/ directory (replaced by LaTeX/)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

LaTeX/README_FIGURES.md

@@ -0,0 +1,135 @@
+# Figure Generation for Copycat Graph Theory Paper
+
+This folder contains Python scripts to generate all figures for the paper "From Hardcoded Heuristics to Graph-Theoretical Constructs."
+
+## Prerequisites
+
+Install Python 3.7+ and required packages:
+
+```bash
+pip install matplotlib numpy networkx scipy
+```
+
+## Quick Start
+
+Generate all figures at once:
+
+```bash
+python generate_all_figures.py
+```
+
+Or run individual scripts:
+
+```bash
+python generate_slipnet_graph.py      # Figure 1: Slipnet graph structure
+python activation_spreading.py        # Figure 2: Activation spreading dynamics
+python resistance_distance.py         # Figure 3: Resistance distance heat map
+python workspace_evolution.py         # Figures 4 & 5: Workspace evolution & betweenness
+python clustering_analysis.py         # Figure 6: Clustering coefficient analysis
+python compare_formulas.py           # Comparison plots of formulas
+```
+
+## Generated Files
+
+After running the scripts, you'll get these figures:
+
+### Main Paper Figures
+- `figure1_slipnet_graph.pdf/.png` - Slipnet graph with conceptual depth gradient
+- `figure2_activation_spreading.pdf/.png` - Activation spreading over time with differential decay
+- `figure3_resistance_distance.pdf/.png` - Resistance distance vs shortest path comparison
+- `figure4_workspace_evolution.pdf/.png` - Workspace graph at 4 time steps
+- `figure5_betweenness_dynamics.pdf/.png` - Betweenness centrality over time
+- `figure6_clustering_distribution.pdf/.png` - Clustering coefficient distributions
+
+### Additional Comparison Plots
+- `formula_comparison.pdf/.png` - 6-panel comparison of all hardcoded formulas vs proposed alternatives
+- `scalability_comparison.pdf/.png` - Performance across string lengths and domain transfer
+- `slippability_temperature.pdf/.png` - Temperature-dependent slippability curves
+- `external_strength_comparison.pdf/.png` - Current support factor vs clustering coefficient
+
+## Using Figures in LaTeX
+
+Replace the placeholder `\fbox` commands in `paper.tex` with:
+
+```latex
+\begin{figure}[htbp]
+\centering
+\includegraphics[width=0.8\textwidth]{figure1_slipnet_graph.pdf}
+\caption{Slipnet graph structure...}
+\label{fig:slipnet}
+\end{figure}
+```
+
+## Script Descriptions
+
+### 1. `generate_slipnet_graph.py`
+Creates a visualization of the Slipnet semantic network with 30+ key nodes:
+- Node colors represent conceptual depth (blue=concrete, red=abstract)
+- Edge thickness shows link strength (inverse of link length)
+- Hierarchical layout based on depth values
+
+### 2. `compare_formulas.py`
+Generates comprehensive comparisons showing:
+- Support factor: 0.6^(1/n³) vs clustering coefficient
+- Member compatibility: Discrete (0.7/1.0) vs continuous structural equivalence
+- Group length factors: Step function vs subgraph density
+- Salience weights: Fixed (0.2/0.8) vs betweenness centrality
+- Activation jump: Fixed threshold (55.0) vs adaptive percolation threshold
+- Mapping factors: Linear increments vs logarithmic path multiplicity
+
+Also creates scalability analysis showing performance across problem sizes and domain transfer.
+
+### 3. `activation_spreading.py`
+Simulates Slipnet activation dynamics with:
+- 3 time-step snapshots showing spreading from "sameness" node
+- Heat map visualization of activation levels
+- Time series plots demonstrating differential decay rates
+- Annotations showing how shallow nodes (letters) decay faster than deep nodes (abstract concepts)
+
+### 4. `resistance_distance.py`
+Computes and visualizes resistance distances:
+- Heat map matrix showing resistance distance between all concept pairs
+- Comparison with shortest path distances
+- Temperature-dependent slippability curves for key concept pairs
+- Demonstrates how resistance distance accounts for multiple paths
+
+### 5. `clustering_analysis.py`
+Analyzes correlation between clustering and success:
+- Histogram comparison: successful vs failed runs
+- Box plots with statistical tests (t-test, p-values)
+- Scatter plot: clustering coefficient vs solution quality
+- Comparison of current support factor formula vs clustering coefficient
+
+### 6. `workspace_evolution.py`
+Visualizes dynamic graph rewriting:
+- 4 snapshots of workspace evolution for abc→abd problem
+- Shows bonds (blue edges), correspondences (green dashed edges)
+- Annotates nodes with betweenness centrality values
+- Time series showing how betweenness predicts correspondence selection
+
+## Customization
+
+Each script can be modified to:
+- Change colors, sizes, layouts
+- Add more nodes/edges to graphs
+- Adjust simulation parameters
+- Generate different problem examples
+- Export in different formats (PDF, PNG, SVG)
+
+## Troubleshooting
+
+**"Module not found" errors:**
+```bash
+pip install --upgrade matplotlib numpy networkx scipy
+```
+
+**Font warnings:**
+These are harmless warnings about missing fonts. Figures will still generate correctly.
+
+**Layout issues:**
+If graph layouts look cluttered, adjust the `k` parameter in `nx.spring_layout()` or use different layout algorithms (`nx.kamada_kawai_layout()`, `nx.spectral_layout()`).
+
+## Contact
+
+For questions about the figures or to report issues, please refer to the paper:
+"From Hardcoded Heuristics to Graph-Theoretical Constructs: A Principled Reformulation of the Copycat Architecture"