Waveframe Labs

Waveframe Labs develops governance infrastructure and enforcement tooling for AI-assisted workflows and reproducible research systems.

The Aurora Ecosystem defines how policy, workflow structure, validation, and execution enforcement interact to produce traceable, governed outputs.

Explore the full ecosystem on GitHub.

The Aurora Stack

Authority flows downward. Enforcement flows upward.
The Aurora stack separates governance, workflow structure, validation, and execution enforcement to ensure that AI-assisted decisions and state transitions remain auditable and accountable.

Governance — Neurotransparency Doctrine, Neurotransparency Specification, Aurora Research Initiative

Method — Aurora Workflow Orchestration (AWO)

L2–L3

Collapsed on this page — Language & Ontology, and Lineage (see system hierarchy)

Validation & Enforcement — Stamp validation engine and CRI-CORE enforcement kernel

Publication & Distribution — Forge-generated artifacts and citable research outputs

Demonstrations — Practical examples of governed workflows and enforcement behavior

The complete L0–L6 architecture (including L2 and L3) is defined in the system hierarchy.

Institutional Log

Claim Lifecycle Demo v0.3.3 released — PyPI dependency support (CRI-CORE) 2026.03.03

Finance Governance Demo v0.3.4 released — citation metadata & changelog 2026.03.03

CRI-CORE v0.6.0 released — first public PyPI distribution 2026.03.02