Data Plane
Noisy, approximate, measured inputs
Measurements, traces, simulations, statistics, and probabilistic outputs live here. This plane is non-canonical by default.
How it works
This page explains the stack in public-facing terms: where noisy inputs live, where the noisy-to-exact transition is made explicit, and where canonical reasoning artifacts come under validator discipline.
The point is not more trace volume. The point is an exact, reviewable transition from approximate evidence into canonical artifacts with stable outputs and compatibility identity.
Three layers
Planes describe operational strata and the discipline applied there. They are not aliases for named components or product modules.
Data Plane
Measurements, traces, simulations, statistics, and probabilistic outputs live here. This plane is non-canonical by default.
Bridge
The bridge makes normalization explicit, prevents hidden float-to-decision jumps, and preserves provenance as approximate input crosses into exact reasoning.
Control Plane
Canonical reasoning artifacts live here under deterministic evaluation structure, decision discipline, and validator-checkable effects.
Planes describe operational strata. Tsubasa, Liu, and Tobi describe stack roles that author or validate artifacts across the disciplined path.
Stack roles
The components below are responsibilities in the stack. They are not synonyms for Data Plane, Bridge, or Control Plane.
Tsubasa
Canonical reasoning language and semantic layer for exact, control-plane meaning.
Liu
Improves authoring ergonomics without relaxing canonical boundaries or redefining semantics.
Tobi
Validates canonical meaning in implementation with deterministic diagnostics and _h compatibility identity.
Canonical pipeline
The canonical path is staged and deterministic. Each step makes the transition into validator-checkable form more explicit.
_h compatibility identity
Surface compatibility-oriented identity for workflow gates.
Bridge discipline
If the transition from noisy measurement or probabilistic output into exact decision logic is hidden, trust collapses. The bridge keeps that transition explicit and reviewable instead of burying it inside implementation detail.
That is what makes the downstream artifact easier to compare, reproduce, validate, and gate across CI and reproducibility-sensitive workflows.
Positioning distinction
These are adjacent concepts, but they answer different questions and should not be collapsed into one another.
Shows what happened in a system, run, or workflow.
Suggests whether behavior seems useful, acceptable, or performant.
Checks whether the artifact is canonical, reproducible, and validator-backed under discipline.
Workflow fit
Keep the next step narrow: use the docs path, review the GitHub-first workflow starter, and contact Organetic only when you need help placing the released validator line inside a real gate.