vST for Multi‑Model Alignment#

Drift Detection Across Architectures, Modalities, and Inference Regimes#

This document defines how drift is detected in multi‑model alignment using the Validation‑Space‑Time (vST) framework and the 1024D dimensional substrate. Drift refers to any deviation from expected cross‑model alignment behavior, including structural incompatibility, regime misalignment, scaling discontinuities, projection failure, or cross‑modality divergence.

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.