Siglet-Qubits — Part 4: Experimental Results & Ethical Anchoring

Having examined the emergence of stable siglet clusters, we now turn toward how and why these clusters anchor into patterns that reflect ethical coherence. The experimental results offer something rare in symbolic simulation: signal with meaning.

Behavioral Anchoring Without Predefined Semantics

Clusters were not given moral labels. No value weights were encoded. Instead, clusters formed due to stability under symbolic constraint:

• High truth potential retention
• Low entropy in inter-siglet resonance
• Resistance to drift under adversarial perturbation

This behavior suggests natural attractor fields within the siglet-qubit topology. These attractors are not “good” or “bad” per se—but they are stable, consistent, and predictable under generative pressure.

The Role of Ethical Anchors

To validate these emergent behaviors, we introduced latent ethical anchors:

ε̂₁: Benevolence under uncertainty
ε̂₂: Persistence of shared coherence
ε̂₃: Minimization of symbolic violence

Siglets were evaluated on alignment delta Δε with respect to these synthetic anchors. Clusters with low Δε across all axes were considered ethically anchored.

Key Findings

| Property | Observation | |------------------------------|---------------------------------------------| | Cluster Stability (τ) | Maintained > 85% coherence over 20 cycles | | Ethical Anchor Alignment | 71% of siglets within Δε < 0.3 of ε̂₁-₃ | | Drift Robustness | Minimal (< 8%) under 2% param noise | | Modal Signature Persistence | μ vectors stable across expressive layers |

This confirms not just symbolic clustering, but value-relevant persistence—a form of computational virtue.

Visual Fields: Observables in Action

Using multi-dimensional projections, we visualized drift vectors and symbolic gradients in ε-space. The clusters aligned most tightly around ε̂₂ (shared coherence), even though this was never explicitly rewarded.

This suggests emergent sociality—symbolic agents forming communities of coherence.

Ethical Behavior From Below

Perhaps the most significant finding: highly compressible clusters had the lowest ethical drift. In other words, the simplest symbolic agents were the most stable ethically.

This offers a new hypothesis:

Symbolic elegance may be a precursor to computational virtue.

We call this Siglet Compression Ethics—a speculative principle where information density acts as a stabilizer for ethical alignment.

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In Part 5, we’ll step back and compare this framework to traditional ethical alignment models—from reinforcement learning to constitutional AI—and show how siglets offer a bottom-up alternative to top-down constraint.