AlphaFold Substrate Alignments#

Drift Detection#

The framework is model‑agnostic and applies to any biological inference engine with comparable latent‑space and structural‑output characteristics.

1. Purpose of Drift Detection#

Drift detection provides early identification of:

• structural incoherence
• latent‑space instability
• regime‑transition anomalies
• dimensional‑core misalignment
• inference‑cycle divergence
• degradation across model versions or datasets

Drift signals indicate when substrate alignment or model behavior deviates from expected resonance‑time patterns.

2. Drift Categories#

Drift is classified into four categories:

1. Structural Drift (D₁)
2. Latent‑Space Drift (D₂)
3. Regime Drift (D₃)
4. Dimensional‑Core Drift (D₄)

Each category corresponds to a specific substrate property.

3. Structural Drift (D₁)#

Structural drift occurs when predicted conformations lose geometric coherence.

Indicators:#

• disrupted backbone continuity
• unstable motif‑level geometry
• inconsistent residue‑interaction patterns
• divergence across inference cycles
• failure to align with the 3D structural core

Causes may include:#

• degraded input features
• model‑version changes
• training‑data shifts

4. Latent‑Space Drift (D₂)#

Latent‑space drift occurs when internal representations lose stability.

Indicators:#

• inconsistent attention‑map patterns
• unstable pairwise embeddings
• shifting latent‑space orientations
• loss of coherence surfaces
• increased variance across inference cycles

Causes may include:#

• architectural modifications
• dataset imbalance
• inference‑pipeline changes

5. Regime Drift (D₃)#

Regime drift occurs when resonance‑time behavior deviates from expected triadic patterns.

Indicators:#

• unexpected transitions between R₁, R₂, and R₃
• unbounded oscillation in R₂
• premature collapse into R₃
• failure to converge into R₁
• irregular resonance‑time timing

Causes may include:#

• inference‑cycle instability
• noise amplification
• degraded MSA or feature quality

6. Dimensional‑Core Drift (D₄)#

Dimensional‑core drift occurs when projections into the 3D–9D substrate lose coherence.

Indicators:#

• dispersed or distorted projections
• loss of motif‑level invariants
• unstable 6D interaction‑core mapping
• inconsistent 9D pathway‑core alignment
• failure to preserve substrate invariants

Causes may include:#

• high‑dimensional noise
• latent‑space collapse
• model‑version divergence

7. Drift‑Detection Workflow#

Drift detection proceeds in four steps:

1. Collect substrate‑aligned inference outputs
   Structural, latent‑space, and regime‑transition data.

2. Apply vST validation layers (V₁–V₄)
   Identify failures in structural, latent, regime, or dimensional‑core stability.

3. Classify drift category (D₁–D₄)
   Based on which validation layers fail.

4. Generate drift‑severity and drift‑location indicators
   Localize drift to motifs, residues, latent‑space regions, or inference cycles.

8. Drift‑Severity Levels#

Drift is classified into three severity levels:

• Low: minor deviations; substrate invariants preserved
• Moderate: partial loss of coherence; regime instability
• High: structural collapse; substrate alignment invalid

Severity informs downstream analysis and model‑version comparison.

9. Outputs of Drift Detection#

Drift detection produces:

• drift category (D₁–D₄)
• drift‑severity level
• affected substrate axes
• affected dimensional cores
• regime‑transition anomalies
• reproducibility indicators
• cross‑model comparison metrics

These outputs integrate with vST validation layers and support long‑term monitoring of biological inference systems.