Synaptopus/docs/ROADMAP.md

Roadmap

Overall Direction

Synaptopus is intended to become a multi-architecture artificial neural systems lab that supports:

• reusable architecture families
• hybrid execution across unlike systems
• inspectable traces for pedagogy and research
• graph-oriented tooling
• browser-based experimentation

The project should remain useful even if no single architecture family dominates it.

Current State

The repository already contains:

• generic runtime and trace primitives
• component-role protocols and cooperative orchestration
• information-theoretic sequence analysis
• generic reporting helpers
• graph schema and trace serialization
• Adaline and a small Madaline-style extension
• multilayer backpropagation
• ART1
• Hopfield-style dynamics and generic Hopfield matrix preparation
• a small XOR novelty demo combining backpropagation and ART1
• a richer parity-pressure demo combining backpropagation and ART1 under category pressure
• a demo exporter that can emit artifacts for multiple internal demos
• first-pass checkpoint/resume snapshots for the internal demos

This is the first point at which Synaptopus is more than a scaffold.

Near Term

• Extend checkpoint/resume beyond internal demos toward a generic snapshot contract
• Add explicit RNG-state capture where demo behavior is stochastic at runtime
• Expose snapshot artifacts more directly in the browser-side tooling
• Document recommended conventions for state, candidate, metadata, and mutable model serialization

Mid Term

• Introduce domain adapters as examples rather than as the center of the framework
• Add experiment runners that generate comparable reports across parameter sweeps
• Add more robust trace viewers and summarized execution statistics
• Build a TypeScript mirror of the graph schema and trace model
• Prototype a browser-based workbench that can visualize execution traces and graph structure
• Add a distinct recurrent backpropagation family rather than overloading the current feedforward reference BP

Longer Term

• Support richer loop and controller semantics in the graph layer
• Add pedagogical views for stepwise inspection of network behavior
• Expand architecture coverage beyond the historically reconstructed families
• Allow the same execution concepts to span music, classification, toy planning, and other problem domains
• Support saved sessions and replayable teaching demonstrations

Design Constraints

Several constraints should remain stable as the repository grows:

• generic code should be preferred over thesis-specific code
• architecture families should remain explicit rather than hidden behind one opaque abstraction
• graph tooling should reflect execution semantics rather than invent a separate model
• serialization should stay JSON-friendly for browser consumption
• pedagogy should be treated as a first-class use case, not an afterthought

Relationship To TriuneCadence

TriuneCadence remains the historically grounded exemplar and compatibility reference for the thesis-derived hybrid composition system.

Synaptopus should borrow generic, reusable pieces from that work, but should not become tied to one domain, one historical artifact set, or one architecture trio.

Concrete Next Milestones

1. Generalize snapshot/resume beyond the built-in demos.
2. Extend the TypeScript-side contracts to cover snapshot artifacts explicitly.
3. Teach the browser tooling to inspect checkpoint contents and resume lineage.
4. Add a more complex mixed-family example with stronger controller semantics than the current parity-pressure demo.