Introduces a 21-page paper focused exclusively on the Copycat Workspace,
proposing three graph-metric substitutions for hardcoded constants and
unifying them under a common information-theoretic framework:
- Clustering coefficient as support vector (replaces 0.6^(1/n^3) factor)
- Betweenness centrality as salience weights (replaces fixed 0.2/0.8 ratios)
- Subgraph density as length factors (replaces step function {5,20,60,90})
Central contribution: all three metrics are ratios actual/maximum in [0,1],
interpretable as probabilities, yielding Shannon self-information in bits.
This common unit enables principled arithmetic via workspace entropy
H(W) = sum(I_C) + sum(I_B) + sum(I_rho), with the conjecture T ∝ H(W).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The slipnet node previously called "bondFacet" was misleading since
"facet" is used throughout the codebase as a property name on Bond,
Group, and Rule objects. Renamed to "bond_edge_type" to better reflect
its role as a descriptor type category. Also removed precomputed
minPathToLetter data from slipnet.json.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Key finding: Eccentricity is the only metric significantly correlated
with conceptual depth (r=-0.380, p=0.029). Local centrality measures
(degree, betweenness, closeness) show no significant correlation.
New files:
- compute_centrality.py: Computes 8 graph metrics
- centrality_comparison.png: Visual comparison of all metrics
- Updated paper with full analysis
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Analysis shows no significant correlation between conceptual depth
and hop distance to letter nodes (r=0.281, p=0.113). Includes
Python scripts, visualizations, and LaTeX paper.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- 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>
