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Roadmap for Future Development

RuleFlow is an active research instrument undergoing continuous theoretical and architectural evolution. The roadmap below represents only a curated snapshot of ongoing explorations and potential directions—not an exhaustive specification. Because our work directly investigates emergent behaviors in discrete complex systems and substitution architectures, planned features, mathematical primitives, and language semantics will adapt dynamically as new research insights emerge.

Architectural Generalization

  • Upgrade the core engine to natively support advanced topologies (2D Grids, 3D Voxels, General Graphs, and Hypergraphs) via a universal interface.
  • Implement advanced graph matching using VF2/VF3 algorithms and the Double-Pushout (DPO) graph rewriting framework.
  • Introduce a Stochastic Rule Engine by adding probability weights to target rules.
  • Formalize the Plugin Framework to allow pure Python function hooks for Custom Selectors and Targets without namespace collisions.
  • Implement a Piece-Table system for highly efficient memory management.

Discovery Tools & Academic Integration

  • Build an Inverse/Regression Suite using machine learning and genetic algorithms to reverse-engineer rulesets that fit specific target sequences or graphs.
  • Program a Causal Analysis Engine capable of running causal analysis on Python (or simple C, MIPs Assembly) code.
  • Implement "Paper-Ready" exports for academic publication, including PyVis HTML graphs, degree assortativity metrics, and formatted LaTeX tables.
  • Integrate the engine with external analytical tools and frameworks, including Gephi, Agentic AI, MCP servers, and CellPyLib.