vST for Multi‑Model Alignment#

Drift Detection Across Architectures, Modalities, and Inference Regimes#

Drift detection is essential for evaluating cross‑architecture comparisons, cross‑modality mappings, training‑run differences, and version‑to‑version compatibility.

1. Purpose of Multi‑Model Drift Detection#

Drift detection enables reproducible evaluation of:

• instability in cross‑model alignment surfaces
• changes in alignment‑regime behavior (A₁ᴴ, A₂ᴴ, A₃ᴴ)
• cross‑architecture compatibility
• scaling‑law continuity across model families
• projection stability into 3D–9D cores
• primitive‑level integrity (DP, TDP‑X, SP‑X, CP‑X)
• coherence‑surface behavior across modalities
• cross‑checkpoint or cross‑sampler divergence

Drift is not inherently negative; it is a structural signal.
The substrate determines whether that signal is stable, transitional, or harmful.

2. Types of Drift#

Drift is classified into four substrate‑aligned categories:

2.1 Structural Drift (D₁ᴹ)#

Deviation in cross‑model alignment geometry.

Indicators

• unstable 3D alignment motifs
• loss of compact cross‑model structure
• abrupt variance spikes across architectures
• incoherent alignment surfaces

Interpretation
Often caused by architectural mismatch, modality divergence, or unstable projection.

2.2 Dimensional Drift (D₂ᴹ)#

Discontinuities in scaling or projection behavior across models.

Indicators

• non‑invertible 9D projections
• fragmentation in 64D–1024D alignment regions
• scaling‑law violations across architectures
• architecture‑dependent divergence

Interpretation
Common when aligning models with different latent dimensionalities or scaling behaviors.

2.3 Alignment‑Regime Drift (D₃ᴹ)#

Unexpected changes in cross‑model regime identity or transitions.

Indicators

• premature transitions into A₃ᴴ
• oscillatory instability in A₂ᴴ
• collapse of stable A₁ᴴ regions
• resonance‑time discontinuities

Interpretation
Signals incompatibility, modality mismatch, or inference‑dynamics divergence.

2.4 Projection Drift (D₄ᴹ)#

Misalignment between heterogeneous latent states and triadic cores.

Indicators

• inconsistent 3D–9D mapping
• loss of primitive‑aligned projection
• divergence across checkpoints or architectures
• incompatible latent‑space geometry

Interpretation
Often appears after architecture changes, modality shifts, or projection‑method adjustments.

3. Drift Detection Signals#

Drift is detected using substrate‑aligned signals:

• variance distribution across models
• coherence‑surface continuity
• primitive‑level stability (DP, TDP‑X, SP‑X, CP‑X)
• resonance‑time behavior
• projection‑stability metrics
• cross‑architecture alignment surfaces
• cross‑modality divergence
• vST validation outputs (V₁–V₄)

These signals collectively determine drift category and severity.

4. Drift Across the Dimensional Ladder#

Drift may appear at different scales:

4.1 64D–128D (Local Alignment Drift)#

• instability in early alignment regions
• boundary tearing in transitional surfaces
• inconsistent cross‑model motifs

4.2 256D–512D (Trajectory‑Level Drift)#

• cross‑architecture divergence
• modality‑dependent instability
• inconsistent alignment transitions
• regime‑transition irregularities

4.3 1024D+ (High‑Dimensional Drift)#

• coherence‑surface collapse
• scaling discontinuities
• projection failure
• chaotic divergence

High‑dimensional drift is the most severe and often indicates deep incompatibility.

5. Cross‑Architecture Drift Detection#

Cross‑architecture drift is detected by comparing:

• alignment‑regime maps
• coherence‑surface geometry
• projection stability
• variance distribution
• primitive‑level structure
• resonance‑time behavior

Drift may arise from:

• architectural mismatch
• training‑run divergence
• latent‑dimension changes
• inference‑dynamics differences

vST provides a consistent substrate for evaluating these changes.

6. Cross‑Modality Drift Detection#

Cross‑modality drift occurs when aligning models from different data domains.

Indicators

• divergence in transitional alignment regions
• inconsistent cross‑modality motifs
• modality‑driven oscillations
• non‑invertible projections

Common sources:

• text ↔ image
• protein ↔ structure
• control ↔ simulation
• embedding ↔ generative

7. Drift Severity Levels#

Drift severity is classified into:

Low Severity#

• minor variance shifts
• stable projections
• no regime collapse

Moderate Severity#

• partial fragmentation
• unstable A₂ᴴ transitions
• inconsistent cross‑model alignment

High Severity#

• collapse of coherence surfaces
• persistent A₃ᴴ behavior
• non‑invertible projections
• loss of primitive‑level compatibility

High‑severity drift indicates a failure of alignment invariants.

8. Drift Detection Workflow#

A substrate‑aligned drift detection workflow:

1. Project heterogeneous latent states into 9D
2. Classify alignment regimes (A₁ᴴ, A₂ᴴ, A₃ᴴ)
3. Evaluate scaling continuity (64D–1024D)
4. Check primitive‑level stability (DP, TDP‑X, SP‑X, CP‑X)
5. Validate with vST layers (V₁–V₄)
6. Compare across architectures, modalities, or checkpoints
7. Assign drift category (D₁ᴹ–D₄ᴹ)
8. Assign drift severity (low, moderate, high)

This workflow is architecture‑agnostic and reproducible.

9. Outputs of Multi‑Model Drift Detection#

Drift detection produces:

• drift category (D₁ᴹ–D₄ᴹ)
• drift severity
• alignment‑regime anomalies
• projection‑stability indicators
• scaling‑law discontinuities
• cross‑architecture and cross‑modality alignment surfaces
• vST validation results

These outputs support governance, interpretability, and version management for multi‑model systems.