v0.2.0 — Expanded Specification and Conformance Model

This release significantly expands and strengthens the Infoconex AI Flywheel Specification.

It completes the v0.2.0 conceptual milestone by refining the lifecycle, principles, learning model, validation requirements, persistence and reuse requirements, supersession model, and evidence-based conformance approach.

Key changes include:

• Clarified the complete Execute → Observe → Evaluate → Classify → Adapt → Validate → Persist → Reuse lifecycle.
• Defined explicit no-change and reinforcing-success paths so improvement does not require adaptation on every cycle.
• Defined validation sufficiency requirements for candidate changes and reusable learning.
• Defined what may qualify as persisted operational learning.
• Defined evidence requirements for demonstrating that persisted learning actually influences future execution.
• Defined how persisted learning may be challenged, superseded, deprecated, invalidated, rolled back, or retired.
• Strengthened the eight principles to reflect the completed lifecycle and learning model.
• Added independently assessable conformance criteria aligned to each principle.
• Updated architecture, examples, terminology, and supporting documentation for consistency with the completed model.
• Prepared the specification and documentation site for v0.2.0.
• Decoupled website asset cache busting from the specification version.

Specification v0.2.0 remains a draft and is intended for implementation testing, public review, further research, and continued refinement.