b5323b5a74
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>
193 lines
4.5 KiB
BibTeX
193 lines
4.5 KiB
BibTeX
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@article{linhares2025graph,
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title={From Hardcoded Heuristics to Graph-Theoretical Constructs: A Principled Reformulation of the Copycat Architecture},
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author={Linhares, Alex},
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journal={Manuscript},
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year={2025},
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note={Paper 1 in this series}
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}
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