Siglet-Qubits — Part 5: Alignment Theory & Comparative Models

If Part 4 gave us ethical dynamics as emergent fields, Part 5 asks: how does this compare to existing AI alignment architectures?

The Alignment Landscape

Mainstream AI alignment has largely followed three paradigms:

1. Reinforcement Learning from Human Feedback (RLHF)

   • Models optimize behavior based on scalar feedback scores.
   • Strength: Scalable; compatible with deep learning.
   • Limitation: Easily gamed. No true understanding of why.

2. Constitutional AI

   • Uses predefined rules (e.g., OpenAI’s principles or Anthropic’s Constitution).
   • Strength: Enforceable guidelines; clearer traceability.
   • Limitation: Brittle in novel edge cases. Lacks symbolic nuance.

3. Symbolic Rule Systems

   • Based on hard-coded logic and constraints.
   • Strength: Verifiable.
   • Limitation: Inflexible, non-adaptive.

Siglet Alignment: A Fourth Way

The siglet-qubit model proposes a bottom-up symbolic emergence:

• No explicit value functions.
• No rigid rulesets.
• No human ratings.

Instead, alignment is measured as coherence under pressure, drift, noise, and symbolic entropy. The system learns not to obey, but to stabilize meaning.

We term this: Reverse-Mapped Consequence Alignment (RMCA).

Core Difference

| Property | Traditional Models | Siglet-Qubits | |---------------------------|--------------------------------|-------------------------------------------| | Feedback Source | Human or Rule-based | Emergent symbolic behavior | | Adaptivity | Tuned via human iteration | Drift-tolerant and entropy-sensitive | | Symbolic Awareness | Low | High (explicit symbolic structure) | | Transparency | Medium | High via trajectory tracing and clustering| | Alignment Guarantee | Prescriptive | Emergent via coherence metrics |

Ethical Observables, Not Declarations

The siglet model doesn’t assert what is “right.”
It tracks what persists under symbolic entropy. What remains intelligible and coherent across agents and noise is treated as ethical attractor space.

This creates a new view of ethics:

Ethics as an emergent field in symbolic computation.

This reorients alignment away from reward and toward resonance.

When to Use This Model

Siglet-qubit models are ideal when:

• You can’t enumerate all possible values or rules.
• You want systems to generalize to future ethical needs.
• You value explainability rooted in structure, not latent weights.

They're not a silver bullet, but they provide a foundation for alignment in worlds with:

• Quantum uncertainty
• Multi-agent entropy
• Non-semantic symbolic exchange

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In Part 6, we’ll explore how this framework translates into real-world systems—from AI safety kernels to inter-agent consensus engines—and outline next steps for research, collaboration, and public deployment.