vST for Large Language Models#

Drift Detection in High‑Dimensional LLM Latent Spaces#

This document defines how drift is detected in Large Language Models (LLMs) using the Validation‑Space‑Time (vST) framework and the 1024D dimensional substrate. Drift refers to any deviation from expected substrate behavior, including structural instability, regime misalignment, scaling discontinuities, or projection failure.

Drift detection is essential for evaluating model updates, fine‑tuning procedures, training interventions, and cross‑version consistency.

1. Purpose of Drift Detection#

Drift detection enables us to:

• identify instability in latent‑space structure
• detect changes in regime behavior (R₁ᴴ, R₂ᴴ, R₃ᴴ)
• evaluate cross‑version compatibility
• monitor scaling‑law continuity
• validate projection stability into 3D–9D cores
• ensure primitive‑level integrity (DP, TDP, SP, CP)
• support governance of model updates and checkpoints

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

2. Types of Drift#

Drift is classified into four substrate‑aligned categories:

2.1 Structural Drift (D₁)#

Deviation in motif‑level geometry or local coherence.

Indicators:

• unstable 3D projections
• loss of compact latent motifs
• abrupt variance spikes

2.2 Dimensional Drift (D₂)#

Discontinuities in dimensional scaling or projection behavior.

Indicators:

• non‑invertible 9D projections
• fragmentation in 64D–1024D latent regions
• scaling‑law violations

2.3 Regime Drift (D₃)#

Unexpected changes in regime identity or transitions.

Indicators:

• premature transitions into R₃ᴴ
• oscillatory instability in R₂ᴴ
• collapse of stable R₁ᴴ regions

2.4 Projection Drift (D₄)#

Misalignment between high‑dimensional states and triadic cores.

Indicators:

• inconsistent 3D–9D mapping
• loss of primitive‑aligned projection
• divergence across layers or tokens

3. Drift Detection Signals#

Drift is detected using substrate‑aligned signals:

• variance distribution across dimensions
• coherence‑surface continuity
• primitive‑level stability (DP, TDP, SP, CP)
• resonance‑time alignment
• projection‑stability metrics
• cross‑version alignment surfaces
• 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 (Embedding Drift)#

• semantic drift
• unstable token embeddings
• loss of local coherence

4.2 256D–512D (Hidden‑State Drift)#

• branching instability
• regime‑transition irregularities
• inconsistent attention patterns

4.3 1024D+ (High‑Dimensional Drift)#

• fragmentation of coherence surfaces
• scaling discontinuities
• projection failure

High‑dimensional drift is the most severe and often indicates training instability.

5. Cross‑Version Drift Detection#

Cross‑version drift is detected by comparing:

• latent‑trajectory regimes
• coherence‑surface geometry
• projection stability
• variance distribution
• primitive‑level structure
• resonance‑time behavior

Drift may arise from:

• fine‑tuning
• RLHF or DPO
• architecture changes
• training‑data shifts
• checkpoint selection

vST provides a consistent substrate for evaluating these changes.

6. Drift Severity Levels#

Drift severity is classified into:

Low Severity#

• minor variance shifts
• stable projections
• no regime collapse

Moderate Severity#

• partial fragmentation
• unstable R₂ᴴ transitions
• inconsistent cross‑layer alignment

High Severity#

• collapse of coherence surfaces
• persistent R₃ᴴ behavior
• non‑invertible projections
• loss of primitive‑level structure

High‑severity drift indicates a failure of substrate invariants.

7. Drift Detection Workflow#

A substrate‑aligned drift detection workflow:

1. Project latent states into 9D
2. Classify regime behavior (R₁ᴴ, R₂ᴴ, R₃ᴴ)
3. Evaluate scaling continuity (64D–1024D)
4. Check primitive‑level stability (DP, TDP, SP, CP)
5. Validate with vST layers (V₁–V₄)
6. Compare across layers, tokens, or versions
7. Assign drift category (D₁–D₄)
8. Assign drift severity (low, moderate, high)

This workflow is model‑agnostic and reproducible.

8. Outputs of Drift Detection#

Drift detection produces:

• drift category (D₁–D₄)
• drift severity
• regime‑transition anomalies
• projection‑stability indicators
• scaling‑law discontinuities
• cross‑version alignment surfaces
• vST validation results

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