Files
copycat/LaTeX/paper.aux
Alex Linhares 06a42cc746 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>
2026-01-29 19:14:01 +00:00

116 lines
13 KiB
TeX

\relax
\providecommand\hyper@newdestlabel[2]{}
\providecommand\HyField@AuxAddToFields[1]{}
\providecommand\HyField@AuxAddToCoFields[2]{}
\citation{mitchell1993analogy,hofstadter1995fluid}
\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}{section.1}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {2}The Problem with Hardcoded Constants}{3}{section.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}Brittleness and Domain Specificity}{3}{subsection.2.1}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}Catalog of Hardcoded Constants}{4}{subsection.2.2}\protected@file@percent }
\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Major hardcoded constants in Copycat implementation. Values are empirically determined rather than derived from principles.}}{4}{table.1}\protected@file@percent }
\newlabel{tab:constants}{{1}{4}{Major hardcoded constants in Copycat implementation. Values are empirically determined rather than derived from principles}{table.1}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.3}Lack of Principled Justification}{4}{subsection.2.3}\protected@file@percent }
\citation{watts1998collective}
\@writefile{toc}{\contentsline {subsection}{\numberline {2.4}Scalability Limitations}{5}{subsection.2.4}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {2.5}Cognitive Implausibility}{5}{subsection.2.5}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {2.6}The Case for Graph-Theoretical Reformulation}{6}{subsection.2.6}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {3}The Slipnet and its Graph Operations}{6}{section.3}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Slipnet as a Semantic Network}{6}{subsection.3.1}\protected@file@percent }
\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Slipnet node types with conceptual depths, counts, and average connectivity. Letter nodes are most concrete (depth 10), while abstract relations have depth 90.}}{7}{table.2}\protected@file@percent }
\newlabel{tab:slipnodes}{{2}{7}{Slipnet node types with conceptual depths, counts, and average connectivity. Letter nodes are most concrete (depth 10), while abstract relations have depth 90}{table.2}{}}
\@writefile{toc}{\contentsline {paragraph}{Category Links}{7}{section*.1}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Instance Links}{7}{section*.2}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Property Links}{7}{section*.3}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Lateral Slip Links}{7}{section*.4}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Lateral Non-Slip Links}{8}{section*.5}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Conceptual Depth as Minimum Distance to Low-Level Nodes}{8}{subsection.3.2}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Slippage via Dynamic Weight Adjustment}{9}{subsection.3.3}\protected@file@percent }
\citation{klein1993resistance}
\@writefile{toc}{\contentsline {subsection}{\numberline {3.4}Graph Visualization and Metrics}{10}{subsection.3.4}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Slipnet graph structure with conceptual depth encoded as node color intensity and link strength as edge thickness.}}{11}{figure.1}\protected@file@percent }
\newlabel{fig:slipnet}{{1}{11}{Slipnet graph structure with conceptual depth encoded as node color intensity and link strength as edge thickness}{figure.1}{}}
\@writefile{toc}{\contentsline {section}{\numberline {4}The Workspace as a Dynamic Graph}{11}{section.4}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Activation spreading over time demonstrates differential decay: shallow nodes (letters) lose activation rapidly while deep nodes (abstract concepts) persist.}}{12}{figure.2}\protected@file@percent }
\newlabel{fig:activation_spread}{{2}{12}{Activation spreading over time demonstrates differential decay: shallow nodes (letters) lose activation rapidly while deep nodes (abstract concepts) persist}{figure.2}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces Resistance distance heat map reveals multi-path connectivity: concepts connected by multiple routes show lower resistance than single-path connections.}}{12}{figure.3}\protected@file@percent }
\newlabel{fig:resistance_distance}{{3}{12}{Resistance distance heat map reveals multi-path connectivity: concepts connected by multiple routes show lower resistance than single-path connections}{figure.3}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}Workspace Graph Structure}{13}{subsection.4.1}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}Graph Betweenness for Structural Importance}{13}{subsection.4.2}\protected@file@percent }
\citation{freeman1977set,brandes2001faster}
\citation{brandes2001faster}
\citation{watts1998collective}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.3}Local Graph Density and Clustering Coefficients}{15}{subsection.4.3}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {4.4}Complete Substitution Table}{16}{subsection.4.4}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {4.5}Algorithmic Implementations}{16}{subsection.4.5}\protected@file@percent }
\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Proposed graph-theoretical replacements for hardcoded constants. Each metric provides principled, adaptive measurement based on graph structure.}}{17}{table.3}\protected@file@percent }
\newlabel{tab:substitutions}{{3}{17}{Proposed graph-theoretical replacements for hardcoded constants. Each metric provides principled, adaptive measurement based on graph structure}{table.3}{}}
\@writefile{loa}{\contentsline {algorithm}{\numberline {1}{\ignorespaces Graph-Based Bond External Strength}}{17}{algorithm.1}\protected@file@percent }
\newlabel{alg:bond_strength}{{1}{17}{Algorithmic Implementations}{algorithm.1}{}}
\@writefile{loa}{\contentsline {algorithm}{\numberline {2}{\ignorespaces Betweenness-Based Salience}}{18}{algorithm.2}\protected@file@percent }
\newlabel{alg:betweenness_salience}{{2}{18}{Algorithmic Implementations}{algorithm.2}{}}
\@writefile{loa}{\contentsline {algorithm}{\numberline {3}{\ignorespaces Adaptive Activation Threshold}}{18}{algorithm.3}\protected@file@percent }
\newlabel{alg:adaptive_threshold}{{3}{18}{Algorithmic Implementations}{algorithm.3}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {4.6}Workspace Evolution Visualization}{18}{subsection.4.6}\protected@file@percent }
\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces Workspace graph evolution during analogical reasoning shows progressive structure formation, with betweenness centrality values identifying strategically important objects.}}{19}{figure.4}\protected@file@percent }
\newlabel{fig:workspace_evolution}{{4}{19}{Workspace graph evolution during analogical reasoning shows progressive structure formation, with betweenness centrality values identifying strategically important objects}{figure.4}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces Betweenness centrality dynamics reveal that objects with sustained high centrality are preferentially selected for correspondences.}}{19}{figure.5}\protected@file@percent }
\newlabel{fig:betweenness_dynamics}{{5}{19}{Betweenness centrality dynamics reveal that objects with sustained high centrality are preferentially selected for correspondences}{figure.5}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces Successful analogy-making runs show higher clustering coefficients, indicating that locally dense structure promotes coherent solutions.}}{20}{figure.6}\protected@file@percent }
\newlabel{fig:clustering_distribution}{{6}{20}{Successful analogy-making runs show higher clustering coefficients, indicating that locally dense structure promotes coherent solutions}{figure.6}{}}
\@writefile{toc}{\contentsline {section}{\numberline {5}Discussion}{20}{section.5}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {5.1}Theoretical Advantages}{20}{subsection.5.1}\protected@file@percent }
\citation{watts1998collective}
\@writefile{toc}{\contentsline {subsection}{\numberline {5.2}Adaptability and Scalability}{21}{subsection.5.2}\protected@file@percent }
\citation{brandes2001faster}
\@writefile{toc}{\contentsline {subsection}{\numberline {5.3}Computational Considerations}{22}{subsection.5.3}\protected@file@percent }
\@writefile{lot}{\contentsline {table}{\numberline {4}{\ignorespaces Computational complexity of graph metrics and mitigation strategies. Here $n$ = nodes, $m$ = edges, $d$ = degree, $m_{sub}$ = edges in subgraph.}}{22}{table.4}\protected@file@percent }
\newlabel{tab:complexity}{{4}{22}{Computational complexity of graph metrics and mitigation strategies. Here $n$ = nodes, $m$ = edges, $d$ = degree, $m_{sub}$ = edges in subgraph}{table.4}{}}
\citation{newman2018networks}
\@writefile{toc}{\contentsline {subsection}{\numberline {5.4}Empirical Predictions and Testable Hypotheses}{23}{subsection.5.4}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Hypothesis 1: Improved Performance Consistency}{23}{section*.6}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Hypothesis 2: Temperature-Graph Entropy Correlation}{23}{section*.7}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Hypothesis 3: Clustering Predicts Success}{23}{section*.8}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Hypothesis 4: Betweenness Predicts Correspondence Selection}{23}{section*.9}\protected@file@percent }
\citation{gentner1983structure}
\citation{scarselli2008graph}
\citation{gardenfors2000conceptual}
\citation{watts1998collective}
\@writefile{toc}{\contentsline {paragraph}{Hypothesis 5: Graceful Degradation}{24}{section*.10}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {5.5}Connections to Related Work}{24}{subsection.5.5}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Analogical Reasoning}{24}{section*.11}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Graph Neural Networks}{24}{section*.12}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Conceptual Spaces}{24}{section*.13}\protected@file@percent }
\citation{newman2018networks}
\@writefile{toc}{\contentsline {paragraph}{Small-World Networks}{25}{section*.14}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Network Science in Cognition}{25}{section*.15}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {5.6}Limitations and Open Questions}{25}{subsection.5.6}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Parameter Selection}{25}{section*.16}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Multi-Relational Graphs}{25}{section*.17}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Temporal Dynamics}{25}{section*.18}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Learning and Meta-Learning}{26}{section*.19}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {5.7}Broader Implications}{26}{subsection.5.7}\protected@file@percent }
\@writefile{toc}{\contentsline {section}{\numberline {6}Conclusion}{26}{section.6}\protected@file@percent }
\citation{forbus2017companion}
\@writefile{toc}{\contentsline {subsection}{\numberline {6.1}Future Work}{27}{subsection.6.1}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Implementation and Validation}{27}{section*.20}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Domain Transfer}{27}{section*.21}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Neuroscience Comparison}{27}{section*.22}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Hybrid Neural-Symbolic Systems}{27}{section*.23}\protected@file@percent }
\@writefile{toc}{\contentsline {paragraph}{Meta-Learning Metric Selection}{27}{section*.24}\protected@file@percent }
\bibstyle{plain}
\bibdata{references}
\bibcite{brandes2001faster}{1}
\bibcite{forbus2017companion}{2}
\bibcite{freeman1977set}{3}
\bibcite{gardenfors2000conceptual}{4}
\@writefile{toc}{\contentsline {paragraph}{Extension to Other Cognitive Architectures}{28}{section*.25}\protected@file@percent }
\@writefile{toc}{\contentsline {subsection}{\numberline {6.2}Closing Perspective}{28}{subsection.6.2}\protected@file@percent }
\bibcite{gentner1983structure}{5}
\bibcite{hofstadter1995fluid}{6}
\bibcite{klein1993resistance}{7}
\bibcite{mitchell1993analogy}{8}
\bibcite{newman2018networks}{9}
\bibcite{scarselli2008graph}{10}
\bibcite{watts1998collective}{11}
\gdef \@abspage@last{29}