zti-delta-algorithm

Automated state inference applies a ZTI+ΔZ cycle to guide state transitions with invariant checks and shadow validation. It uses a YAML delta-block template and a validation script to determine if deterministic changes can be safely integrated.

Auditable state changes are tracked using DELTA_BLOCK templates accompanying every transition. This ensures traceability and rollback readiness while enforcing strict consistency before updates are applied to the system.

Shadow validation is a mechanism within the ΔZ cycle that verifies proposed state changes against strict invariants. It ensures risk-aware approvals by checking transitions in a shadow state before determining integration eligibility.

Validation is performed by running the validate_delta.py script against the filled YAML delta-block template. The script executes invariant checks to determine whether the delta-based change can be integrated into the system.

Yes, pyyaml is required as a dependency to parse the YAML delta-block templates. This parsing enables the validate_delta.py script to execute invariant checks and manage deterministic state changes.

Yes, state inference supports rollback readiness by generating auditable DELTA_BLOCK templates for every state change. These templates provide traceability and enforce strict consistency verification before integration.