Generalized_Notation_Notation (GNN)

Active Inference offers a unifying account of perception, learning, and action under the free energy principle, yet the generative models at its core are still communicated ad hoc: scattered across prose descriptions, bespoke notebooks, and framework-specific code that rarely agree. This fragmentation makes published models hard to reproduce, compare, or port between tools, and it raises the barrier for newcomers learning the formalism. GeneralizedNotationNotation (GNN) addresses this gap with a standardized, human- and machine-readable text language for specifying Active Inference generative models, paired with a 25-step processing pipeline that transforms a single specification into validation, visualization, simulation, and analysis artifacts. A GNN file denotes a generative model, and the language lets that model take the several shapes the field actually uses: the notation blocks a file declares — categorical A/B/C/D[/E] tensors, linear-Gaussian system matrices F/H/Q/R with a Gaussian prior, per-agent and per-level compositions — fix its model kind, which the pipeline classifies structurally and carries through rendering and execution, recording explicitly which registered backends can render and run a model of each kind and which report it unsupported. GNN's "Triple Play" treats each model as three coordinated views — a textual specification, graphical visualizations, and executable cognitive models — so that one source yields consistent outputs across modalities. The framework spans 32 exemplar specifications (27 discrete-state and 5 continuous linear-Gaussian) across 9 model families and 10 registered rendering backends, with explicit gates for cross-format semantic fidelity and cross-framework reliability. GNN 3.0.0 layered safe-by-design long-running orchestration on top of this language and pipeline — durable observation streams, resumable run sessions, and auditable container plans that generate, validate, and replay data only, with no live infrastructure mutation. We describe the language, the model kinds it denotes, the pipeline architecture, and the validation that confirms specifications round-trip faithfully across formats and execute across multiple simulation backends — with coverage gaps recorded as explicit profiled-unsupported statuses rather than silently omitted — establishing GNN as reproducible, interoperable infrastructure for communicating Active Inference models.

Overview source: Archived manuscript abstract.

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