# 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.