Alignment

Canonical Definition

A system is aligned when its internal constraints, adaptive behavior, and emergent capabilities remain compatible with its intended stability regime and boundary conditions.

Alignment is not a static property. It is an ongoing control problem under changing environments, incentives, and capability transitions.

Alignment Components

• Ground (Reference): stable observation, noise suppression, reliable measurement.
• Structure (Constraint): governance, memory, continuity, rule clarity.
• Dynamics (Adaptation): learning, exploration, feedback integration.
• Emergence (Capability): controlled novelty, bounded discontinuity, safe phase transitions.

Misalignment Signatures

• Weak Ground: measurement noise, unreliable feedback, reactive oscillation.
• Weak Structure: incoherence, policy drift, loss of identity continuity.
• Excess Dynamics: exploration without convergence, instability via drift.
• Runaway Emergence: capability jumps without constraint integration.

Alignment failures typically cascade: loss of reference degrades constraint enforcement, which amplifies drift and increases the probability of unsafe emergence.

Alignment as Control

Alignment can be treated as a closed-loop control system:

Observe (Ground)
→ Constrain (Structure)
→ Adapt (Dynamics)
→ Transition (Emergence)
→ Re-stabilize (Ground/Structure)

Healthy systems continuously rebalance these phases. Unsafe systems skip re-stabilization and accumulate instability debt.

Human, Organizational, and AI Alignment

The same structural problem appears across substrates:

• Human alignment: coherent intent under emotion, uncertainty, and fatigue (reference + regulation).
• Organizational alignment: incentives, governance, and execution coherence under changing environments.
• AI alignment: capability growth with bounded behavior under explicit constraints and reliable feedback.

The canon provides a shared language for diagnosing alignment failures without collapsing into ideology.