Add paper 2: information-theoretic unification of workspace graph metrics

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>

LaTeX/paper2/references.bib

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+@article{linhares2025graph,
+  title={From Hardcoded Heuristics to Graph-Theoretical Constructs: A Principled Reformulation of the Copycat Architecture},
+  author={Linhares, Alex},
+  journal={Manuscript},
+  year={2025},
+  note={Paper 1 in this series}
+}